Clinical and MRI Markers for Acute vs Chronic TMD Using a Machine Learning and Deep Learning Approach

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Statistical, machine learning, and deep learning models were used for analysis. The study include 239 patients with TMD (161 women and 78 men; mean age 35.60 ± 17.93 years), diagnosed using Diagnostic Criteria for TMD (Axis I). Participants were categorized into: acute TMD (< 6 months) and chronic TMD (≥ 6 months) (51.05%). Significance clinical findings revealed that temporomandibular joint (TMJ) noise and bruxism were more frequently reported in patients with chronic TMD than in those with acute TMD. The visual analog scale (VAS) score, reflecting subjective pain intensity, was significantly higher in chronic TMD than in acute TMD. Additionally, patients with chronic TMD had shorter average sleep durations than their acute counterparts. STOP-Bang total scores were higher in chronic TMD (3.02 ± 2.09 vs. 2.39 ± 1.73, p = 0.012) than in acute TMD. Magnetic resonance imaging revealed structural abnormalities in patients with chronic TMD, with significantly higher rates of anterior disc displacement (ADD), TMJ osteoarthritis, and joint space narrowing. Using logistic regression—a widely recognized machine learning model—the AUROC for predicting chronic TMD was 0.7550 (95% CI]: 0.6550–0.8550). Significant predictors of chronic TMD included TMJ noise, bruxism, VAS score, sleep disturbance, STOP-Bang total score ≥ 5 (high risk of obstructive sleep apnea), ADD, and joint space narrowing. Deep learning (multilayered perceptron) improved prediction performance by 3.99% over logistic regression (79.49% vs. 75.50%, p = 0.3067). These findings may help clinicians prevent symptom chronicity and mitigate the progression to chronic TMD, ultimately improving patient care. Health sciences/Biomarkers/Predictive markers Health sciences/Biomarkers/Diagnostic markers Health sciences/Risk factors temporomandibular disorder chronic disc displacement machine learning deep learning magnetic resonance imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Temporomandibular disorders (TMD) encompass diverse conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures, often resulting in pain, joint noise, and limited mandibular function 1 , 2 . TMD is a common musculoskeletal pain disorder affecting the orofacial region, affecting approximately 31% of adults and older individuals, with women 1.5 to 2.24 times more likely to be affected than men 3 , 4 . Pain ranges from acute cases, where symptoms persist for < 6 months, to chronic cases that extend beyond 6 months 5 . Chronic TMD poses greater management challenges owing to its persistent nature, which often leads to worse clinical outcomes 6 . Therefore, identifying the factors associated with the progression from acute to chronic TMD is crucial for effective intervention and prevention. Chronic TMD, like other chronic pain conditions, has significant clinical implications. Persistent pain in patients with TMD is often associated with reduced quality of life, impairing essential functions such as chewing, speaking, and sleep 7 . In addition, chronic TMD can lead to psychological distress, including anxiety and depression, contributing to the perpetuation of pain through central sensitization mechanisms 8 , 9 . This cycle of pain, aggravated symptoms, and psychological distress necessitates early recognition and management to prevent chronicity and improve long-term outcomes. However, research exploring signs and symptoms that distinguish acute from chronic TMD, along with the key predictors driving the transition to chronic TMD, remains limited. Structural abnormalities observed on magnetic resonance imaging (MRI) and behavioral factors such as sleep disturbances and bruxism may contribute to chronic TMD 10 . However, the complex interaction between these factors makes the accurate prediction of chronicity challenging. Recent advancements in artificial intelligence, particularly machine learning (ML) and deep learning (DL), offer new opportunities to systematically analyze large datasets and uncover hidden patterns that may not be evident through traditional clinical assessments alone 11 , 12 . Identifying predictive markers for chronic TMD is essential for timely intervention, especially when behavioral and structural factors can be addressed to prevent disease progression. This study aimed to identify clinical, sleep, and MRI-related factors associated with chronic TMD using statistical, ML, and DL models. We hypothesize that combining statistical methods with AI-based models would enhance understanding of chronic TMD. We compared the predictive performance of logistic regression, a well-established ML algorithm, with that of multi-layer perceptron (MLP), a DL model, to assess its effectiveness in predicting chronic TMD. By identifying key predictors and patterns, this study aimed to provide insight to guide clinicians in developing personalized treatment strategies to prevent the adverse effects of chronicity and improve patient outcomes. Results 1) Demographics Among the 239 patients with TMD, 117 (48.95%) were diagnosed with acute TMD, and 122 (51.05%) with chronic TMD. The female-to-male ratio across the sample was 161:78 (2.06:1). Notably, no statistically significant differences were observed in age or sex between the acute and chronic groups. The mean age of patients with acute TMD was 36.36 ± 17.69 years, while that of those with chronic TMD was 34.88 ± 18.20 years (p = 0.524). Similarly, the female-to-male ratio did not differ significantly between acute TMD (31.6% male vs. 68.4% female; 2.16:1) and chronic TMD (33.6% male vs. 66.4% female; 1.98:1, p = 0.783) (Table 1 ). Table 1 Demographics and clinical characteristics of TMD patients Acute TMD (n = 117) Chronic TMD (n = 122) p-value mean ± SD or n (%) mean ± SD or n (%) Demographics Age 36.36 ± 17.69 34.88 ± 18.20 0.524 Sex Male 37 (31.6%) 41 (33.6%) 0.783 Female 80 (68.4%) 81 (66.4%) Symptom duration (month) 0.77 ± 0.67 26.75 ± 36.37 < 0.001*** Sleep time (hour) 7.37 ± 2.17 6.41 ± 2.37 < 0.001*** VAS 3.64 ± 2.38 4.82 ± 2.47 < 0.001*** Clinical symptoms TMJ noise 61 (52.1%) 86 (70.5%) 0.005** TMD pain 91 (77.8%) 96 (78.7%) 0.877 Locking 53 (45.3%) 50 (41.0%) 0.516 Muscle stiffness 60 (51.3%) 52 (43.0%) 0.242 Bruxism 18 (15.4%) 38 (31.1%) 0.006** Tinnitus 39 (33.3%) 33 (27.0%) 0.325 Contributing factors for TMD Sleep problem 26 (22.2%) 43 (35.2%) 0.032* Psychological stress 53 (45.3%) 54 (44.3%) 0.897 Macrotrauma 12 (10.3%) 8 (6.6%) 0.355 The results were obtained using the χ2 test with Bonferroni adjustment, and the mean differences between the two TMD groups were calculated using the t-test. Statistical significance was set at p < 0.05. *: p < 0.05, **: p < 0.01, ***: p < 0.001. TMD, temporomandibular disorder; VAS, visual analog scale; TMJ, temporomandibular joint; STOP-Bang, snoring, tiredness, observed apnea, high blood pressure (); BMI, age, neck circumference, and gender (Bang), SD, standard deviation. 2) Clinical characteristics The duration of symptoms in acute TMD was 0.77 ± 0.67 months, while in chronic TMD it was 26.75 ± 36.37 months (p < 0.001). The VAS score, reflecting subjective pain intensity, was significantly higher in chronic TMD (4.82 ± 2.47) compared to acute TMD (3.64 ± 2.38, p < 0.001). Among the six clinical symptoms evaluated, significant group differences between the acute and chronic TMD groups were observed for TMJ noise and bruxism. TMJ noise was more frequently reported in patients with chronic TMD than in those with acute TMD (52.1% vs. 70.5%; p = 0.005). Similarly, bruxism was significantly more frequent in patients with chronic TMD (31.1%) than in those with acute TMD (15.4%; p = 0.006). The most common clinical symptom was TMD pain, which was observed in 77.8% of the acute TMD cases and 78.7% of the chronic TMD cases; however, no significant differences were observed between the two groups (p = 0.877). Tinnitus was present in 33.3% of patients with acute TMD and 27.0% of patients with chronic TMD (p = 0.325). Notably, no significant differences were observed between the two TMD groups in terms of locking, muscle stiffness, or tinnitus. Regarding contributing factors, psychological stress was observed at similar frequencies in both groups (45.3% vs. 44.3%, p = 0.897), as was microtrauma (10.3% vs. 6.6%, p > 0.05) (Table 1 ). 3) Sleep-related factor Sleep time was significantly shorter in chronic TMD (6.41 ± 2.37 hours) compared to acute TMD (7.37 ± 2.17 hours, p < 0.001). Sleep problems were significantly more frequent in the chronic TMD group (35.2%) than in the acute TMD group (22.2%; p = 0.032) (Table 1 ). In the STOP-Bang questionnaire, significant differences were observed in the proportion of patients who answered "yes" to items 1 (Snoring), 3 (Observed apnea), and 5 (Body mass index ≥ 35 kg/m²). Specifically, patients with chronic TMD reported these symptoms more frequently than those with acute TMD: STOP-Bang 1 (20.5% vs. 39.3%, p = 0.002), STOP-Bang 3 (13.7% vs. 35.2%, p < 0.001), and STOP-Bang 5 (15.4% vs. 27.0%, p = 0.039). In contrast, no significant differences were observed between the two groups in the proportions of patients reporting the other items: STOP-Bang 2 (Tiredness), 4 (High blood pressure), 6 (Age ≥ 50 years), 7 (Neck circumference > 40 cm), and 8 (Male gender). The STOP-Bang total score (STOP-Bang score) was significantly higher in chronic TMD patients compared to acute TMD patients (3.02 ± 2.09 vs. 2.39 ± 1.73, p = 0.012). Additionally, the proportion of patients with a STOP-Bang score ≥ 3 was significantly higher in chronic TMD (59.8%) than in acute TMD (44.4%, p = 0.020). Similarly, the proportion of patients with a STOP-Bang score ≥ 5 was also significantly higher in chronic TMD (27.0%) compared to acute TMD (12.0%, p = 0.003) (Table 2 ). Table 2 Results of the STOP-Bang Questionnaire Acute TMD (n = 117) Chronic TMD (n = 122) p-value mean ± SD or n (%) mean ± SD or n (%) STOP-Bang 1 (Snoring) 24 (20.5%) 48 (39.3%) 0.002** STOP-Bang 2 (Tiredness) 65 (55.6%) 73 (59.8%) 0.515 STOP-Bang 3 (Observed apnea) 16 (13.7%) 43 (35.2%) < 0.001*** STOP-Bang 4 (High blood pressure) 74 (63.2%) 77 (63.1%) 1.000 STOP-Bang 5 (Body mass index) 18 (15.4%) 33 (27.0%) 0.039* STOP-Bang 6 (Age over 50) 17 (14.5%) 20 (16.4%) 0.724 STOP-Bang 7 (Neck circumference) 26 (22.2%) 28 (23.0%) 1.000 STOP-Bang 8 (Male gender) 37 (31.6%) 41 (33.6%) 0.783 STOP-Bang total score 2.39 ± 1.73 3.02 ± 2.09 0.012* STOP-Bang ≥ 3 52 (44.4%) 73 (59.8%) 0.020* STOP-Bang ≥ 5 14 (12.0%) 33 (27.0%) 0.003** The results were obtained using the χ2 test with Bonferroni adjustment. Statistical significance was set at p < 0.05. *: p < 0.05, **: p < 0.01, ***: p < 0.001. TMD: temporomandibular disorder, STOP-Bang snoring, tiredness, observed apnea, high blood pressure (), BMI, age, neck circumference, and gender (Bang). SD, standard deviation; SD, standard deviation. 4) MRI findings In the MRI findings of patients with TMD, the most frequently observed features were as follows: in acute TMD, the order was joint space narrowing > effusion > ADD > TMJ-OA, whereas in chronic TMD, the order was joint space narrowing > ADD > TMJ-OA > effusion. The prevalences of ADD (60.7% vs. 86.9%, p < 0.001), TMJ-OA (58.1% vs. 82.0%, p < 0.001), and joint space narrowing (64.1% vs. 88.5%, p < 0.001) were significantly higher in patients with chronic TMD than in those with acute TMD (Fig. 1 ). Although effusion was more frequent in the chronic TMD group (71.3%) than in the acute TMD group (62.4%), the difference was not statistically significant (p = 0.169). Excluding effusion, the other three MRI findings showed statistically significant differences between the two groups (Table 3 ). Table 3 Comparison of MRI findings between acute and chronic TMD groups MRI findings Acute TMD (n = 117) n (%) Chronic TMD (n = 122) n (%) p-value ADD 71 (60.7%) 106 (86.9%) < 0.001*** TMJ-OA 68 (58.1%) 100 (82.0%) < 0.001*** Joint space narrowing 75 (64.1%) 108 (88.5%) < 0.001*** Effusion 73 (62.4%) 87 (71.3%) 0.169 The results were obtained using the χ2 test with Bonferroni adjustment. Statistical significance was set at p < 0.05. *: p < 0.05, **: p < 0.01, ***: p < 0.001. TMD, temporomandibular disorder; TMJ, temporomandibular joint; ADD, anterior disc displacement; TMJ-OA, TMJ osteoarthritis 5) Regression models for chronic TMD prediction Single logistic regression analysis was used to examine whether each factor served as a significant predictor of chronic TMD compared to acute TMD. Joint space narrowing emerged as the most powerful predictor, increasing the likelihood of developing chronic TMD by 4.320 times compared with acute TMD (OR = 4.320, 95% CI: 2.204–8.466, p < 0.001). ADD (anterior disc displacement) significantly increased the likelihood of chronic TMD by 4.292 times (OR = 4.292, 95% CI: 2.256–8.168, p < 0.001). Similarly, TMJ-OA was associated with a 3.275-fold increase in the likelihood of chronic TMD compared to acute TMD (OR = 3.275, 95% CI: 1.816–5.908, p < 0.001). Other significant predictors for chronic TMD included bruxism (OR = 2.488, 95% CI: 1.323–4.680, p = 0.005), STOP-Bang score ≥ 5 (OR = 2.728, 95% CI: 1.373–5.420), TMJ noise (OR = 2.193, 95% CI: 1.288–3.733, p = 0.004), sleep problems (OR = 1.905, 95% CI: 1.075–3.733, p = 0.004), STOP-Bang score ≥ 5 (OR = 1.862, 95% CI: 1.114–3.113), and an increase in VAS score (OR = 1.220, 95% CI: 1.094–1.361, p < 0.001). Conversely, an increase in sleep time reduced the likelihood of developing chronic TMD by 0.826 times (OR = 0.826, 95% CI: 0.733–0.931, p = 0.002). In the multiple logistic regression analysis with backward selection, all previously mentioned factors were included simultaneously to determine their collective impact on the development of chronic TMD and the extent to which each factor contributed. Among these predictors, bruxism was identified as the most powerful predictor, increasing the likelihood of chronic TMD by 4.048 times (OR = 4.048, 95% CI: 1.786–9.173, p = 0.01) (Table 4 ). Table 4 Single and multiple regression analysis for predicting chronic TMD Analysis 1: Single logistic regression analysis Analysis 2: Multiple logistic regression analysis with backward selection Demographics OR Lower 95% CI Upper 95% CI p-value OR Lower 95% CI Upper 95% CI p-value Sleep time 0.826 0.733 0.931 0.002** 0.757 0.648 0.885 < 0.001*** VAS 1.220 1.094 1.361 < 0.001*** 1.306 1.125 1.516 < 0.001*** TMJ noise 2.193 1.288 3.733 0.004** 2.023 1.036 3.949 0.0389* Bruxism 2.488 1.323 4.680 0.005** 4.048 1.786 9.173 0.001** Sleep problem 1.905 1.075 3.378 0.027* 1.966 0.954 4.051 0.067 ADD 4.292 2.256 8.168 < 0.001*** 3.871 1.113 13.458 0.033* TMJ-OA 3.275 1.816 5.908 < 0.001*** 3.323 1.583 6.975 0.002** Effusion 1.498 0.871 2.576 0.144 0.546 0.260 1.144 0.109 Joint space narrowing 4.320 2.204 8.466 < 0.001*** 2.097 0.595 7.389 0.249 STOP-Bang ≥ 3 1.862 1.114 3.113 0.018* 2.167 1.047 4.483 0.037* STOP-Bang ≥ 5 2.728 1.373 5.420 0.004** 2.484 0.926 6.662 0.071 The results were obtained using single and multiple regression analyses of chronic TMD. Statistical significance was set at p < 0.05. *: p < 0.05, **: p < 0.01, ***: p < 0.001. TMD, temporomandibular disorder; VAS, visual analog scale; TMJ, temporomandibular joint; ADD, anterior disc displacement; TMJ-OA, osteoarthritis of the TMJ; STOP-Bang, snoring, tiredness, observed apnea, high blood pressure (STOP)-BMI, age, neck circumference, and sex (Bang); SD, standard deviation; OR, odds ratio; CI, confidence interval. *: Variables with an absolute weight value of 0.4 or higher are marked with an asterisk. The weight values range from − 1 to + 1, where the absolute value of the weight indicates its predictive power. A weight value closer to zero suggests lower predictive strength, indicating that the corresponding variable has little impact on the model's prediction. In contrast, a weight value closer to 1 (or -1) indicates a higher predictive strength, signifying that the variable plays a more significant role in the prediction process. A positive weight implied a direct relationship, whereas a negative weight indicated an inverse relationship. 6) Cut-off value for predicting chronic TMD Figure 3 presents the cut-off values for sleep time, VAS, and STOP-Bang total scores in predicting chronic TMD. Among these, sleep time showed the strongest predictive power, with a cut-off value of 6.750 h (area under the curve [AUC] = 0.614, 95% confidence interval [CI]: 0.543–0.685, p = 0.002). This suggests that individuals who sleep less than 6.750 hours are more likely to develop chronic TMD. Similarly, the VAS score was also a significant predictor of chronic TMD, with a cut-off value of 4.5 (AUC = 0.647, 95% CI: 0.567–0.707, p < 0.001). A VAS score higher than 4.5 indicates a significantly increased risk of developing chronic TMD. The STOP-Bang total score had a cut-off value of 2.50 (AUC = 0.594, 95% CI: 0.522–0.667, p = 0.012), with scores above this threshold significantly increasing the likelihood of chronic TMD. 7) Correlations between the clinical characteristics and MRI findings When analyzing the entire dataset, the chronicity of TMD symptoms was associated with ADD (r = 0.30), joint space narrowing (r = 0.29), TMJ OA (r = 0.26), and effusion (r = 0.09). Additionally, symptom chronicity was associated with clinical factors such as bruxism (r = 0.19), TMJ noise (r = 0.19), and TMD pain (r = 0.01). The factors that most correlated with symptom chronicity were ADD (r = 0.30), joint space narrowing (r = 0.29), TMJ OA (r = 0.26), TMJ noise (r = 0.19), bruxism (r = 0.19), effusion (r = 0.09), and TMD pain (r = 0.01). Furthermore, ADD exhibited the strongest correlation with joint space narrowing (r = 0.82), along with positive correlations with TMJ-OA (r = 0.24) and effusion (r = 0.17). TMJ-OA showed a significant positive correlation with ADD (r = 0.24), effusion (r = 0.20), and joint space narrowing (r = 0.20). Effusion was also correlated with ADD (r = 0.17), TMJ-OA (r = 0.20), and joint space narrowing (r = 0.12). A similar pattern of correlations was observed when focusing on acute TMD, consistent with the findings from the entire dataset. However, a stronger relationship was observed between ADD and joint space narrowing (r = 0.86). The correlation between effusion and ADD was also stronger than that observed in the whole dataset (r = 0.21), whereas the relationship between TMJ OA and ADD was slightly weaker (r = 0.20) compared to the whole dataset. For chronic TMD, the correlation between TMJ-OA and effusion was stronger than that for the entire dataset (r = 0.27). However, the interrelationships among ADD, effusion, and joint space narrowing were weaker in chronic TMD than in the entire dataset and acute TMD. 8) 2D and 3D visualization of interrelationships In the 2D visualization, factors significantly associated with chronic TMD (Euclidean distance ≤ 0.2) were identified. Among the MRI findings, the key factors were ADD, TMJ-OA, and joint space narrowing. For clinical characteristics, TMJ noise, bruxism, VAS, and sleep time were significantly associated with chronic TMD, with a STOP-Bang total score ≥ 5, also approaching a Euclidean distance of 0.2. A 3D network was used to visualize the relationships among variables associated with chronic TMD. This visualization depicts the degree of interconnection between the factors associated with chronic TMD and their relationships with other variables. Among the clinical characteristics linked to chronic TMD, TMJ noise and bruxism are interrelated. Additionally, the VAS score was related to chronic TMD and was associated with effusion and sleep problems. MRI findings associated with chronic TMD include ADD, joint space narrowing of the TMJ, and TMJ-OA. The ADD demonstrated a strong relationship with joint space narrowing. 9) Machine learning algorithms for chronic TMD We employed logistic regression model within machine learning to identify significant predictors of chronic TMD and extracted the weight values for each factor, as presented in Fig. 5 . This figure shows the variables useful for predicting chronic TMD and highlights their respective weight values. Significant positive predictors, defined by an absolute weight value of 0.4 or higher, were identified in the following order: bruxism (0.6768), VAS (0.5812), sleep problem (0.5230), joint space narrowing (0.5116), TMJ noise (0.4457), ADD (0.4337), and STOP-Bang ≥ 5 (0.4082). Thus, the presence or increase in these factors is associated with a higher likelihood of chronic TMD. Conversely, sleep duration was the only significant negative predictor. As sleep time decreases (weight = -0.5926), the likelihood of developing chronic TMD increases. 10) Comparison of prediction performance between machine learning and deep learning Logistic regression, a commonly used machine learning model, was employed to predict chronic TMD based on 19 clinical characteristics and MRI findings. The model achieved an AUROC of 0.7550 (95% confidence interval [CI], 0.6550–0.8550). The prediction accuracy for chronic TMD was 0.7083, with higher sensitivity (73.4%) compared to specificity (68.4%). When using Multi-Layer Perceptron (MLP), a type of deep learning model, to predict chronic TMD, the AUROC improved to 0.7949 (95% CI: 0.6949–0.8949). However, the difference in the AUROC between logistic regression and MLP was not statistically significant (p = 0.3067). In other words, the prediction performance of deep learning (MLP) was 3.99% higher than that of logistic regression (79.49% vs. 75.50%); however, the difference was not statistically significant (p = 0.3067). The MLP model achieved a diagnostic accuracy of 77.08% for chronic TMD compared to acute TMD. Similar to the logistic regression, MLP demonstrated higher sensitivity (79.3%) than specificity (73.7%) (Fig. 6 ). Discussion This study provides valuable insights into the distinguishing characteristics of acute and chronic TMD, focusing on clinical, sleep-related, and MRI-based predictors of chronicity. Our findings revealed that clinical and behavioral factors, including TMJ noise, bruxism, sleep problems, and elevated STOP-B Bang scores, were more prevalent in patients with chronic TMD than in those with acute TMD. Furthermore, structural abnormalities detected using MRI, such as anterior disc displacement (ADD), TMJ osteoarthritis, and joint space narrowing, highlight the relevance of joint-related changes in the chronicity of TMD symptoms. These findings underscore the multifaceted nature of chronic TMD and emphasize the importance of early intervention to prevent symptom persistence and deterioration. Identifying these key predictors will help clinicians proactively manage TMD and improve long-term patient outcomes. Pain, sleep disturbance, and chronic symptoms are closely interconnected 24 , 25 . This study confirmed that patients with chronic TMD experience greater subjective pain, as reflected by higher VAS scores and shorter sleep durations, compared to those with acute TMD. These findings highlight that high pain intensity is significantly associated with chronic TMD, and sleep duration is notably shorter in patients with chronic TMD than in those with acute symptoms. These behavioral patterns suggest that chronic pain is closely linked to sleep disturbances, which is consistent with prior research showing that poor sleep quality exacerbates pain perception and impairs recovery from musculoskeletal conditions 26 . Elevated STOP-Bang scores in patients with chronic TMD further underscore the need to address sleep-related breathing disorders such as OSA as part of TMD management strategies. Although a complete consensus has not been reached, OSA is considered a potential risk factor for TMD 27 . Emerging studies have suggested that OSA and TMD share several overlapping mechanisms—such as sleep disturbances, bruxism, and inflammatory responses, which may contribute to the development or exacerbation of TMD symptoms 28 , 29 . Both conditions can disrupt normal sleep patterns, leading to increased pain sensitivity and psychological distress, which further complicates clinical outcomes 30 . In this study, machine learning revealed that a high risk of OSA, as indicated by a high STOP-Bang total score, is a significant predictor of chronic TMD. As research continues to explore this relationship, identifying OSA as a risk factor for TMD holds promise for integrated therapeutic approaches targeting both conditions. These findings are consistent with those of previous studies, suggesting that early intervention targeting joint health may prevent long-term damage and improve clinical outcomes. The MRI findings indicate that structural changes in the TMJ play a significant role in the transition from acute to chronic TMD. The higher prevalence of ADD, TMJ OA, and joint space narrowing in patients with chronic TMD supports the hypothesis that prolonged TMD contributes to joint deterioration and persistent symptom. TMJ noise is common in acute TMD and may be linked to ADD 9 , 31 . ADD plays a pivotal role in the progression of chronic TMD by initiating structural changes 32 . As ADD develops, it may gradually lead to joint space narrowing due to increased friction between the mandibular condyle, temporal bone, or articular disc 33 , 34 . This friction can further contribute to the development of TMJ-OA over time. In contrast, effusion—a fluid accumulation within the TMJ—was observed in over 60% of patients with TMD, with a prevalence of 62.4% in acute TMD and 71.3% in chronic TMD; however, effusion did not emerge as a distinguishing predictor of chronic TMD. Despite its relatively high frequency, effusion demonstrates a weak association with other MRI abnormalities. This suggests that, while effusion might reflect an inflammatory response, it is not a reliable indicator of the transition from acute to chronic TMD or the presence of other structural changes such as ADD, joint space narrowing, or TMJ OA. From a diagnostic perspective, both the logistic regression and deep learning models exhibited high diagnostic performance in predicting chronic TMD, demonstrating their potential utility in clinical practice. This finding suggests that while advanced AI models, such as deep learning, offer slight predictive advantages by identifying complex patterns in data, simpler machine learning models, such as logistic regression, remain valuable for clinical applications 35 . Their strength lies in their ease of interpretation, which is crucial for clinicians making both time-sensitive and time-consuming decisions 36 . These tools can enhance patient outcomes by minimizing diagnostic errors and improving decision-making efficiency, thus enhancing patient outcomes. Moreover, the transparency and ease of interpretation of logistic regression make it a practical and reliable option for scenarios where clinical understanding and rapid insight are critical 36 . As deep learning methods continue to advance and gain wider acceptance in healthcare 37 , traditional machine learning models, such as logistic regression, remain essential, offering a balance between predictive power and clinical usability. In addition to general statistics, our attempt at this time includes 2D and 3D visualization as well as AI-based analysis, which can significantly help in understanding the complex interrelationships associated with chronic TMD. Bruxism, characterized by the involuntary clenching or grinding of teeth, is a significant contributing factor to the development and persistence of chronic TMD 38 – 40 . This condition can occur during sleep (sleep bruxism) or while awake (awake bruxism), and both forms are associated with excessive loading of the TMJ and masticatory muscles 41 , 42 . Repetitive mechanical stress from bruxism can exacerbate joint wear and tear, increasing the likelihood of structural abnormalities such as ADD and TMJ OA 43 . These changes strongly correlate with symptom chronicity, as confirmed by the higher prevalence of both bruxism and MRI abnormalities in patients with chronic TMD than in those with acute TMD. Behaviorally, bruxism can disrupt sleep, contributing to poor sleep quality and increased pain sensitivity, further complicating TMD management 25 , 28 . This is consistent with the findings of the present study, in which patients with chronic TMD reported a higher frequency of bruxism and shorter sleep duration. Addressing bruxism through targeted interventions, such as behavioral therapy, occlusal appliances, or stress management, may help reduce joint strain, improve sleep quality 44 , and ultimately prevent the progression from acute to chronic TMD. This emphasizes the need for clinicians to proactively evaluate and manage bruxism in patients proactively, given its pivotal role in the persistence and progression of symptoms. This study suggests that incorporating clinical symptoms, including bruxism, sleep patterns, and structural abnormalities, into treatment strategies may help mitigate the progression to chronic TMD. However, this study has certain limitations. First, the study retrospective design, may have introduced selection bias and limited the ability to establish causal relationships between the predictors and chronic TMD. Second, the sample size, although calculated to achieve adequate statistical power, was drawn from a single institution, potentially affecting the generalizability of the findings to a broader population. Future studies are needed to validate these results across multiple institutions and diverse populations. Third, while the STOP-Bang questionnaire was used to assess OSA risk, polysomnographic evaluation provided more accurate data on sleep disturbance. Fourth, although the study incorporated advanced AI models, the performance of the deep learning model was only marginally better than that of logistic regression, suggesting that further optimization and the inclusion of more comprehensive datasets may be required to enhance predictive accuracy. Finally, behavioral and psychological factors were assessed based on patient self-reports, which are subject to recall bias and may affect the reliability of the data. Future studies should consider longitudinal designs and include objective behavioral assessments to provide more robust evidence regarding the predictors of chronic TMD. Conclusion This study demonstrated that the progression from acute to chronic TMD is influenced by a combination of clinical, behavioral, and structural factors. TMJ noise, bruxism, high VAS scores, sleep disturbances, and specific MRI abnormalities were significant predictors of chronic TMD. AI-based models, particularly deep learning, enhance the explanatory power of these predictors and aid in identifying patients at risk of chronicity. Although deep learning offers slight improvements in predictive performance, traditional machine learning models remain valuable because of their interpretability and ease of use. Clinicians can leverage these findings to develop personalized treatment strategies, emphasize early intervention to prevent symptom chronicity, improve clinical outcomes, and enhance the quality of life in patients with TMD. Methods The research protocol for this study was reviewed to ensure compliance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Kyung Hee University Dental Hospital in Seoul, South Korea (KHD IRB, IRB No-KH-DT24025). Informed consent was obtained from all participants prior to their inclusion in the study. Study population The study population comprised 239 consecutive patients with TMD (161 women and 78 men; mean age 35.60 ± 17.93 years) who visited Kyung Hee University Dental Hospital between January 2020 and September 2024. All diagnoses were made by a specialist with more than 10 years of clinical experience following the diagnostic criteria for TMD Axis I 13 . Patients with TMD were identified, and all clinical reports and MRI images of the TMJs were retrospectively reviewed. The duration of TMD symptoms, as reported by the patients, was recorded in months, with 6 months used as the threshold to classify the patients into two groups: acute TMD (symptom duration < 6 months) and chronic TMD (symptom duration ≥ 6 months) 14 . This study compared the clinical and MRI characteristics associated with chronic TMD to those observed in acute TMD and examined factors contributing to the prolonged duration of symptoms. The exclusion criteria were history of severe injuries, such as unstable multiple trauma to the orofacial area and maxillary and mandibular fractures; systemic diseases potentially affecting the TMJ, such as rheumatic diseases, systemic osteoarthritis, pregnancy, psychological problems, psychiatric or neurological disorders; and cases in which the structure of the TMJ complex was not clearly distinguishable on MRI 15 . Sample size The sample size was calculated using G*Power version 3.1.9.7 (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). A total of 134 participants (alpha error, 0.05; actual power, 0.95) were included in the target sample, and 239 patients with TMD were recruited. Pain intensity Pain intensity was assessed using the visual analog scale (VAS). The VAS score ranged from 0 to 10, with 0 indicating no pain and 10 indicating the worst imaginable pain. Clinical symptoms and contributing factors for TMD Six clinical symptoms were investigated in the patients with TMD: TMJ noise, TMD pain, locking, muscle stiffness, bruxism, and tinnitus. The presence of each parameter was recorded based on patient reports and assessed dichotomously as either “yes” or “no.”. The criteria for each significant complaint were as follows: (1) TMJ noise: sounds such as clicking and crepitus originating from the TMJ during both functional and nonfunctional movement of the mandible, (2) TMD pain: pain associated with TMD involving the TMJ structures, (3) Locking: jaw locking with a mouth opening of < 35 mm, indicating limited mouth opening, as reported by the patient, (4) Muscle stiffness: stiffness, heaviness, or discomfort in muscles during function or at rest, (5) Bruxism: clenching or grinding of teeth while awake or sleeping, and (6) Tinnitus: A perception of various sounds, such as ringing or buzzing, without any corresponding external source. The three contributing factors included sleep disturbance, psychological stress, and a history of macro-trauma, with each factor assessed in a dichotomous manner. STOP-Bang and sleep time The STOP-Bang questionnaire was used to evaluate factors associated obstructive sleep apnea (OSA). This validated screening tool is designed to identify individuals with a high likelihood of OSA. The STOP-Bang questionnaire consists of eight dichotomous (yes/no) questions related to the clinical features of sleep apnea. Each question, a response “yes” scores 1, a “no” response scores 0, and the total score ranges from 0 to 8. The exposure of interest was classified as either binary-low or high likelihood for OSA; the low likelihood of OSA: Yes to < 3 questions, moderate likelihood of OSA: Yes to ≥ 3 questions, and high likelihood of OSA: Yes to ≥ 5 questions 16 , 17 . All patients were instructed to complete the STOP-BANG questionnaire. Additionally, we collected the average self-reported sleep time over the past two weeks in hours. MR image acquisition High-resolution MRIs were obtained using a 3T MRI system (Signa™ Genesis, GE Healthcare, Chicago, IL, USA) with a 6-cm × 8-cm diameter surface coil. The MRI examinations were performed using the MR sequences and protocols of the Kyung Hee University Medical Center. All scans involved sagittal oblique sections (section thickness, ≤ 3 mm; field of view, 15 cm; matrix dimensions, 256 × 224), and spin-echo sagittal MRIs were obtained on axial localizer images. T2-weighted images (T2WIs) were obtained using a 650/14 repetition time (TR)/echo time (TE) and 2650/82 TR/TE sequences. Proton density (PD) images were obtained using a 2650/82 TR/TE sequence. MRI protocols for TMJ evaluation were performed as described previously 2 , 18 . MRI abnormal findings ADD, TMJ-OA, joint space narrowing, and effusion were coded dichotomously as positive or negative. The left and right sides of the patients with bilateral TMJ and ADD were evaluated separately using T2-weighted (T2WI) and proton density (PD) images. If an abnormal finding was present on either side, it was recorded as 'positive. MRI indicators for assessing ADD in patients with TMD were defined as follows 19 : a positive finding of ADD was identified when the posterior band of the articular disc was displaced anteriorly beyond the normal range in the closed-mouth position. A positive finding of TMJ-OA was indicated by the presence of cortical erosion, subchondral cysts, osteophyte formation, flattening, or sclerosis, either alone or in combination 20 . However, the presence of flattening and sclerosis alone is insufficient to diagnose TMJ-OA. Joint space narrowing was defined as a distance of less than 1.5 mm between the outer lines of the condyle and temporal bone 21 . A positive finding of TMJ effusion was recorded when a high-intensity signal was observed in the superior or inferior joint space on closed-mouth sagittal T2WI or PD-weighted images 22 . Intra-examiner reproducibility yielded intra-class correlation coefficients (ICCs) of 0.78 and 0.85, while inter-examiner ICCs for TMJ effusion diagnosis were 0.79 and 0.83, respectively. Disagreements were resolved through discussion until a consensus was reached. AI-based evaluation and visualization The neural network model used in this study follows a multi-layer perceptron (MLP) architecture designed to perform a binary classification of chronic TMD against acute TMD. The dataset was divided into 60% training, 20% validation, and 20% for testing. The input layer received clinical data (features), which were standardized to ensure that the mean of all input features was zero, and the standard deviation was 1. This was followed by four hidden layers that were sequentially connected, with each layer consisting of 128, 64, 32, and 16 neurons, respectively, gradually decreasing in size. Each hidden layer applies the ReLU activation function to introduce nonlinearity, thereby enhancing the model’s ability to learn complex patterns. To prevent overfitting, a dropout rate of 30% was applied to each hidden layer. The output layer consisted of two output nodes for binary classification. CrossEntropyLoss was used as the loss function, and softmax was applied to perform the classification. The activation function was applied in the final output layer. The model was trained for 100 epochs, and the training loss, validation loss, and accuracy were recorded for each epoch. The Adam optimizer was employed due to its ability to handle sparse gradients and noisy data efficiently, as well as its adaptive learning rate, which allows for faster convergence compared to standard stochastic gradient descent. The optimizer combines the advantages of momentum and RMSProp, making it particularly effective for deep-learning tasks. The model with the best performance, defined as the one with the lowest validation loss, was selected and evaluated using the test set to determine its final accuracy. The performance of the MLP deep learning model was compared to that of traditional machine learning methods to assess its effectiveness in predicting chronic TMD. The diagnostic performance of the deep learning model was compared to that of conventional machine learning models (logistic regression model). To advance beyond an isolated understanding of the relationships between variables, we aimed to provide a comprehensive and intuitive understanding through 2D (two-dimensional) and 3D (three-dimensional) visualizations of the relationships between chronic TMD and related variables. During training, the model stored the weights at the point at which the validation loss was minimized, thereby ensuring optimal performance. Code availability The code for the deep learning algorithm developed in this study for predicting chronic TMD is available on GitHub at https://github.com/SeonggwangJeon/Chronic_VAS_visualization/tree/main . Statistics Data were analyzed using IBM SPSS Statistics for Windows (version 26.0; IBM Corp., Armonk, NY, USA). Descriptive statistics are reported as mean ± standard deviation or frequencies with percentages, as appropriate. The distributions of categorical data were assessed using the χ² test with Bonferroni correction for equality of proportions. Student’s t-tests were used to compare the mean values between the two TMD groups. Cramer's V analysis was also used to assess the strength of the associations between the two variables; the statistical values ranged from 0 to 1, with values closer to 1 indicating a stronger correlation. To predict chronic TMD compared to acute TMD, we identified variables with significant differences in means or percentages between acute and chronic TMD groups using t-tests, χ² tests, and Bonferroni correction. The contribution of each selected variable was further evaluated using a single logistic regression analysis, with the results expressed as odds ratios (ORs) and 95% confidence intervals (CIs). To assess the combined predictive power of these factors for chronic TMD, we performed multiple logistic regression analysis with backward selection. All analyses were considered statistically significant at a two-tailed p-value of < 0.05. The performance of the prediction model for chronic TMD was evaluated by plotting the receiver operating characteristic (ROC) curve against the area under the ROC curve (AUC) calculated for each AI model. AUC values were interpreted as follows: AUC = 0.5 (no discrimination), 0.6 ≥ AUC > 0.5 (poor discrimination), 0.7 ≥ AUC > 0.6 (acceptable discrimination), 0.8 ≥ AUC > 0.7 (excellent discrimination); and AUC > 0.9 (outstanding discrimination) 23 . Declarations Acknowledgments The authors extend their special thanks to Sung-Woo Lee of the Department of Oral Medicine and Oral Diagnosis at Seoul National University and to Jung-Pyo Hong of the Department of Orofacial Pain and Oral Medicine at Kyung Hee University Dental Hospital. Informed consent Informed consent was obtained from all patients prior to participation in the study. Author contributions Writing and original draft preparation, Y-HL; conceptualization, Y-HL; methodology, Y-HL; software, Y-HL; validation and formal analysis, Y-HL, Q-SA, and J-HL; investigation, Y-HL and SJ; resources, Y-HL and Q-SA; data curation, Y-HL; writing, review, and editing, Y-HL; visualization, Y-HL; supervision, Y-HL and Y-KN; project administration, Y-HL and Y-KN; and funding acquisition, Y-HL. All the authors contributed to and approved the submission of the manuscript. Conflict of interest The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as conflicts of interest. Data availability The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request. Ethics statement The research protocol complied with the Declaration of Helsinki and was approved by the Institutional Review Board of Kyung Hee University Dental Hospital in Seoul, South Korea (IRB No-KH- No-KH-DT24025). Funding: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2020R1F1A1070072, No. RS-2024-0042120), IITP/MSIT (IITP-2021-0-02068, RS-2020-II201373, RS-2023-00220628), and Kyung Hee University in 2021 (KHU-20211863). References Palmer J, Durham J (2021) Temporomandibular disorders. BJA Educ 21:44–50. 10.1016/j.bjae.2020.11.001 Lee YH, Lee KM, Auh QS (2021) MRI-Based Assessment of Masticatory Muscle Changes in TMD Patients after Whiplash Injury. J Clin Med 10. 10.3390/jcm10071404 Warren MP, Fried JL (2001) Temporomandibular disorders and hormones in women. 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Jpn Dent Sci Rev 58:124–136. 10.1016/j.jdsr.2022.02.004 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Communications Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5336211","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":375054167,"identity":"63438543-8888-406e-9676-c89a34bf8ccb","order_by":0,"name":"Yeon-Hee Lee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYJACiYQKCTl+ECuhgFgtH87YGEs2gLQYEKlFcmZbWuKGAyAmMVr4xQ4fvM3Ddjhx8/nViR8eGDDI84sdIGDD7LRkax6ew8bbbrzdLAF0mOHM2Qn4tRjczjGT5pE4LLvtxtkNIC0JBreJ0mJwmHHzjLObfxCtRXJGQpriBv7ebcTZAvKLxYcDNsYSN3i3WSQYSBD2C7908sEbif+AUdl/dvPNHxU28vzSBLQggARYpQSxysH2HSBF9SgYBaNgFIwkAABmBkY6UviVWwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7323-0411","institution":"Kyung Hee University Dental Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yeon-Hee","middleName":"","lastName":"Lee","suffix":""},{"id":375054168,"identity":"19f0cf48-d769-4a32-a17d-ac1990621c56","order_by":1,"name":"Seonggwang Jeon","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Seonggwang","middleName":"","lastName":"Jeon","suffix":""},{"id":375054169,"identity":"64edce32-95f2-46c9-8478-f63ee4b812bc","order_by":2,"name":"Q-Schick Auh","email":"","orcid":"","institution":"Kyung Hee University Dental Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Q-Schick","middleName":"","lastName":"Auh","suffix":""},{"id":375054170,"identity":"91c871a3-be13-4dda-aa3c-7ecbcdd1ee0a","order_by":3,"name":"Jeong- Hoon Lee","email":"","orcid":"","institution":"Stanford University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jeong-","middleName":"Hoon","lastName":"Lee","suffix":""},{"id":375054171,"identity":"331eed33-11c6-468a-abe4-fccf9366142f","order_by":4,"name":"Yung-Kyun Noh","email":"","orcid":"","institution":"Hanyang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yung-Kyun","middleName":"","lastName":"Noh","suffix":""}],"badges":[],"createdAt":"2024-10-26 07:50:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5336211/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5336211/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43856-025-01081-5","type":"published","date":"2025-09-29T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71810303,"identity":"259970e6-d3b9-44f2-a64a-2acac92e464e","added_by":"auto","created_at":"2024-12-18 18:19:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":508480,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMRI Findings in acute and chronic TMD patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA: Distribution of MRI findings, including anterior disc displacement (ADD), TMJ osteoarthritis (OA), effusion, and joint space narrowing, in acute and chronic TMD cases. B: MRI of acute TMD (1 month) showing ADD with effusion. C: MRI of acute TMD (2 months) displaying ADD. D: MRI of chronic TMD (10 months) showing joint space narrowing along with TMJ OA, ADD, and disc deformity. E: MRI of chronic TMD (2 years) demonstrating both ADD and TMJ OA. All images are proton density MRI scans, which provide detailed soft-tissue contrast essential for evaluating TMJ structural changes. \u003c/strong\u003eTMJ: temporomandibular joint, c: mandibular condyle, d: articular disc, e: effusion. The results were obtained using the χ2 test with Bonferroni adjustment\u003cstrong\u003e.\u003c/strong\u003e Statistical significance was set at p \u0026lt; 0.05. ***\u003cstrong\u003e: p \u0026lt; 0.001.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5336211/v1/68cfa12888de6c9b055d3e82.png"},{"id":71810301,"identity":"9f1cd7fd-f2c9-44ef-81ea-9d81d908f97a","added_by":"auto","created_at":"2024-12-18 18:19:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":650837,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between key clinical characteristics and MRI findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTMD: temporomandibular disorder; Chronicity: A chronic state in which TMD symptoms have persisted for more than six months; TMJ: temporomandibular joint; ADD: anterior disc displacement; TMJ OA: osteoarthritis of the temporomandibular joint. A: Correlation between MRI findings across the entire dataset; B: Correlation between MRI findings in patients with acute TMD; and C: Correlation between MRI findings in patients with chronic TMD. The closer the absolute value of the correlation coefficient is to 1, the stronger the relationship between the two variables. Values near zero indicated little or no correlation.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5336211/v1/c5e1b7e50ae2ac42b43df6db.png"},{"id":71811345,"identity":"93477219-eb6f-47e3-a24b-e05dac54dfb5","added_by":"auto","created_at":"2024-12-18 18:35:14","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":397249,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCut-off value for predicting chronic TMD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVAS, visual analog scale; AUC, area under curve; \u003cstrong\u003eSTOP-Bang, snoring, tiredness, observed apnea, high blood pressure (STOP)-BMI, age, neck circumference, and gender (Bang). When using acute TMD as a reference, the significant predictors for chronic TMD included the VAS score with a cut-off value of 4.50, STOP-Bang score with a cut-off value of 2.50, and sleep time with a cut-off value of 6.75 hours.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5336211/v1/3c57679dd5aa3140a81de897.jpeg"},{"id":71810048,"identity":"ca88c853-18fb-40a7-83e0-7331e428e54c","added_by":"auto","created_at":"2024-12-18 18:11:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":619626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D and 3D Plots of Factors Associated with Chronic TMD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTMJ: temporomandibular disorder, TMJ OA: osteoarthritis on temporomandibular disorder, ADD: anterior disc displacement, VAS: visual analogue scale, STOP-Bang: \u003cstrong\u003esnoring, tiredness, observed apnea, high blood pressure (STOP)-BMI, age, neck circumference, and gender (Bang)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5336211/v1/0ad96eb4d2acb745ea59cdc3.png"},{"id":71811142,"identity":"46b11a0f-4733-4d18-b6bd-8a34ebae98f6","added_by":"auto","created_at":"2024-12-18 18:27:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":225735,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictors of Chronic TMD Identified through Machine Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e*: Variables with an absolute weight value of 0.4 or higher are marked with an asterisk. The weight values range from -1 to +1, where the absolute value of the weight indicates its predictive power. A weight value closer to zero suggests lower predictive strength, indicating that the corresponding variable has little impact on the model's prediction. In contrast, a weight value closer to 1 (or -1) indicates a higher predictive strength, signifying that the variable plays a more significant role in the prediction process. A positive weight implied a direct relationship, whereas a negative weight indicated an inverse relationship.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5336211/v1/4a7cc8091da54cf701a2e4e1.png"},{"id":71810052,"identity":"f7cb7757-c128-4691-b769-ef3243b812ae","added_by":"auto","created_at":"2024-12-18 18:11:14","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":531949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of prediction performance between machine learning and deep learning\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5336211/v1/104298a7c724054697f560ab.jpeg"},{"id":92474140,"identity":"6971bd20-dd56-4d75-963c-cdfc0854be43","added_by":"auto","created_at":"2025-09-30 07:09:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4761059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5336211/v1/1c99daf9-15cc-4011-958f-e69d1ca9cb34.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Clinical and MRI Markers for Acute vs Chronic TMD Using a Machine Learning and Deep Learning Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTemporomandibular disorders (TMD) encompass diverse conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures, often resulting in pain, joint noise, and limited mandibular function \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. TMD is a common musculoskeletal pain disorder affecting the orofacial region, affecting approximately 31% of adults and older individuals, with women 1.5 to 2.24 times more likely to be affected than men \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Pain ranges from acute cases, where symptoms persist for \u0026lt;\u0026thinsp;6 months, to chronic cases that extend beyond 6 months \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Chronic TMD poses greater management challenges owing to its persistent nature, which often leads to worse clinical outcomes \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Therefore, identifying the factors associated with the progression from acute to chronic TMD is crucial for effective intervention and prevention.\u003c/p\u003e \u003cp\u003eChronic TMD, like other chronic pain conditions, has significant clinical implications. Persistent pain in patients with TMD is often associated with reduced quality of life, impairing essential functions such as chewing, speaking, and sleep \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In addition, chronic TMD can lead to psychological distress, including anxiety and depression, contributing to the perpetuation of pain through central sensitization mechanisms \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This cycle of pain, aggravated symptoms, and psychological distress necessitates early recognition and management to prevent chronicity and improve long-term outcomes. However, research exploring signs and symptoms that distinguish acute from chronic TMD, along with the key predictors driving the transition to chronic TMD, remains limited.\u003c/p\u003e \u003cp\u003eStructural abnormalities observed on magnetic resonance imaging (MRI) and behavioral factors such as sleep disturbances and bruxism may contribute to chronic TMD \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, the complex interaction between these factors makes the accurate prediction of chronicity challenging. Recent advancements in artificial intelligence, particularly machine learning (ML) and deep learning (DL), offer new opportunities to systematically analyze large datasets and uncover hidden patterns that may not be evident through traditional clinical assessments alone \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Identifying predictive markers for chronic TMD is essential for timely intervention, especially when behavioral and structural factors can be addressed to prevent disease progression.\u003c/p\u003e \u003cp\u003eThis study aimed to identify clinical, sleep, and MRI-related factors associated with chronic TMD using statistical, ML, and DL models. We hypothesize that combining statistical methods with AI-based models would enhance understanding of chronic TMD. We compared the predictive performance of logistic regression, a well-established ML algorithm, with that of multi-layer perceptron (MLP), a DL model, to assess its effectiveness in predicting chronic TMD. By identifying key predictors and patterns, this study aimed to provide insight to guide clinicians in developing personalized treatment strategies to prevent the adverse effects of chronicity and improve patient outcomes.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1) Demographics\u003c/h2\u003e \u003cp\u003eAmong the 239 patients with TMD, 117 (48.95%) were diagnosed with acute TMD, and 122 (51.05%) with chronic TMD. The female-to-male ratio across the sample was 161:78 (2.06:1). Notably, no statistically significant differences were observed in age or sex between the acute and chronic groups. The mean age of patients with acute TMD was 36.36\u0026thinsp;\u0026plusmn;\u0026thinsp;17.69 years, while that of those with chronic TMD was 34.88\u0026thinsp;\u0026plusmn;\u0026thinsp;18.20 years (p\u0026thinsp;=\u0026thinsp;0.524). Similarly, the female-to-male ratio did not differ significantly between acute TMD (31.6% male vs. 68.4% female; 2.16:1) and chronic TMD (33.6% male vs. 66.4% female; 1.98:1, p\u0026thinsp;=\u0026thinsp;0.783) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographics and clinical characteristics of TMD patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute TMD (n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChronic TMD (n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDemographics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.36\u0026thinsp;\u0026plusmn;\u0026thinsp;17.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.88\u0026thinsp;\u0026plusmn;\u0026thinsp;18.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81 (66.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSymptom duration (month)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.75\u0026thinsp;\u0026plusmn;\u0026thinsp;36.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep time (hour)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical symptoms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTMJ noise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (52.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMD pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle stiffness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (51.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBruxism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.006**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTinnitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContributing factors for TMD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (35.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.032*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (44.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrotrauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe results were obtained using the χ2 test with Bonferroni adjustment, and the mean differences between the two TMD groups were calculated using the t-test. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. TMD, temporomandibular disorder; VAS, visual analog scale; TMJ, temporomandibular joint; STOP-Bang, snoring, tiredness, observed apnea, high blood pressure (); BMI, age, neck circumference, and gender (Bang), SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2) Clinical characteristics\u003c/h3\u003e\n\u003cp\u003eThe duration of symptoms in acute TMD was 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67 months, while in chronic TMD it was 26.75\u0026thinsp;\u0026plusmn;\u0026thinsp;36.37 months (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The VAS score, reflecting subjective pain intensity, was significantly higher in chronic TMD (4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47) compared to acute TMD (3.64\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAmong the six clinical symptoms evaluated, significant group differences between the acute and chronic TMD groups were observed for TMJ noise and bruxism. TMJ noise was more frequently reported in patients with chronic TMD than in those with acute TMD (52.1% vs. 70.5%; p\u0026thinsp;=\u0026thinsp;0.005). Similarly, bruxism was significantly more frequent in patients with chronic TMD (31.1%) than in those with acute TMD (15.4%; p\u0026thinsp;=\u0026thinsp;0.006). The most common clinical symptom was TMD pain, which was observed in 77.8% of the acute TMD cases and 78.7% of the chronic TMD cases; however, no significant differences were observed between the two groups (p\u0026thinsp;=\u0026thinsp;0.877). Tinnitus was present in 33.3% of patients with acute TMD and 27.0% of patients with chronic TMD (p\u0026thinsp;=\u0026thinsp;0.325). Notably, no significant differences were observed between the two TMD groups in terms of locking, muscle stiffness, or tinnitus. Regarding contributing factors, psychological stress was observed at similar frequencies in both groups (45.3% vs. 44.3%, p\u0026thinsp;=\u0026thinsp;0.897), as was microtrauma (10.3% vs. 6.6%, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e3) Sleep-related factor\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSleep time was significantly shorter in chronic TMD (6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37 hours) compared to acute TMD (7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17 hours, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Sleep problems were significantly more frequent in the chronic TMD group (35.2%) than in the acute TMD group (22.2%; p\u0026thinsp;=\u0026thinsp;0.032) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the STOP-Bang questionnaire, significant differences were observed in the proportion of patients who answered \"yes\" to items 1 (Snoring), 3 (Observed apnea), and 5 (Body mass index\u0026thinsp;\u0026ge;\u0026thinsp;35 kg/m\u0026sup2;). Specifically, patients with chronic TMD reported these symptoms more frequently than those with acute TMD: STOP-Bang 1 (20.5% vs. 39.3%, p\u0026thinsp;=\u0026thinsp;0.002), STOP-Bang 3 (13.7% vs. 35.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and STOP-Bang 5 (15.4% vs. 27.0%, p\u0026thinsp;=\u0026thinsp;0.039). In contrast, no significant differences were observed between the two groups in the proportions of patients reporting the other items: STOP-Bang 2 (Tiredness), 4 (High blood pressure), 6 (Age\u0026thinsp;\u0026ge;\u0026thinsp;50 years), 7 (Neck circumference\u0026thinsp;\u0026gt;\u0026thinsp;40 cm), and 8 (Male gender). The STOP-Bang total score (STOP-Bang score) was significantly higher in chronic TMD patients compared to acute TMD patients (3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09 vs. 2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73, p\u0026thinsp;=\u0026thinsp;0.012). Additionally, the proportion of patients with a STOP-Bang score\u0026thinsp;\u0026ge;\u0026thinsp;3 was significantly higher in chronic TMD (59.8%) than in acute TMD (44.4%, p\u0026thinsp;=\u0026thinsp;0.020). Similarly, the proportion of patients with a STOP-Bang score\u0026thinsp;\u0026ge;\u0026thinsp;5 was also significantly higher in chronic TMD (27.0%) compared to acute TMD (12.0%, p\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the STOP-Bang Questionnaire\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute TMD (n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChronic TMD (n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 1 (Snoring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e24 (20.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e48 (39.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 2 (Tiredness)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (55.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.515\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 3 (Observed apnea)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16 (13.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e43 (35.2%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 4 (High blood pressure)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77 (63.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 5 (Body mass index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e18 (15.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e33 (27.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.039*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 6 (Age over 50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (16.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 7 (Neck circumference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTOP-Bang 8 (Male gender)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTOP-Bang total score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.012*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTOP-Bang\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e52 (44.4%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e73 (59.8%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.020*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTOP-Bang\u003c/b\u003e\u0026thinsp;\u0026ge;\u0026thinsp;\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14 (12.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e33 (27.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe results were obtained using the χ2 test with Bonferroni adjustment. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. TMD: temporomandibular disorder, STOP-Bang snoring, tiredness, observed apnea, high blood pressure (), BMI, age, neck circumference, and gender (Bang). SD, standard deviation; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e4) MRI findings\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the MRI findings of patients with TMD, the most frequently observed features were as follows: in acute TMD, the order was joint space narrowing\u0026thinsp;\u0026gt;\u0026thinsp;effusion\u0026thinsp;\u0026gt;\u0026thinsp;ADD\u0026thinsp;\u0026gt;\u0026thinsp;TMJ-OA, whereas in chronic TMD, the order was joint space narrowing\u0026thinsp;\u0026gt;\u0026thinsp;ADD\u0026thinsp;\u0026gt;\u0026thinsp;TMJ-OA\u0026thinsp;\u0026gt;\u0026thinsp;effusion. The prevalences of ADD (60.7% vs. 86.9%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TMJ-OA (58.1% vs. 82.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and joint space narrowing (64.1% vs. 88.5%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly higher in patients with chronic TMD than in those with acute TMD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although effusion was more frequent in the chronic TMD group (71.3%) than in the acute TMD group (62.4%), the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.169). Excluding effusion, the other three MRI findings showed statistically significant differences between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of MRI findings between acute and chronic TMD groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute TMD (n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChronic TMD (n\u0026thinsp;=\u0026thinsp;122)\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71 (60.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106 (86.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTMJ-OA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68 (58.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJoint space narrowing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75 (64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108 (88.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87 (71.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe results were obtained using the χ2 test with Bonferroni adjustment. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. TMD, temporomandibular disorder; TMJ, temporomandibular joint; ADD, anterior disc displacement; TMJ-OA, TMJ osteoarthritis\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e5) Regression models for chronic TMD prediction\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSingle logistic regression analysis was used to examine whether each factor served as a significant predictor of chronic TMD compared to acute TMD. Joint space narrowing emerged as the most powerful predictor, increasing the likelihood of developing chronic TMD by 4.320 times compared with acute TMD (OR\u0026thinsp;=\u0026thinsp;4.320, 95% CI: 2.204\u0026ndash;8.466, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). ADD (anterior disc displacement) significantly increased the likelihood of chronic TMD by 4.292 times (OR\u0026thinsp;=\u0026thinsp;4.292, 95% CI: 2.256\u0026ndash;8.168, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, TMJ-OA was associated with a 3.275-fold increase in the likelihood of chronic TMD compared to acute TMD (OR\u0026thinsp;=\u0026thinsp;3.275, 95% CI: 1.816\u0026ndash;5.908, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other significant predictors for chronic TMD included bruxism (OR\u0026thinsp;=\u0026thinsp;2.488, 95% CI: 1.323\u0026ndash;4.680, p\u0026thinsp;=\u0026thinsp;0.005), STOP-Bang score\u0026thinsp;\u0026ge;\u0026thinsp;5 (OR\u0026thinsp;=\u0026thinsp;2.728, 95% CI: 1.373\u0026ndash;5.420), TMJ noise (OR\u0026thinsp;=\u0026thinsp;2.193, 95% CI: 1.288\u0026ndash;3.733, p\u0026thinsp;=\u0026thinsp;0.004), sleep problems (OR\u0026thinsp;=\u0026thinsp;1.905, 95% CI: 1.075\u0026ndash;3.733, p\u0026thinsp;=\u0026thinsp;0.004), STOP-Bang score\u0026thinsp;\u0026ge;\u0026thinsp;5 (OR\u0026thinsp;=\u0026thinsp;1.862, 95% CI: 1.114\u0026ndash;3.113), and an increase in VAS score (OR\u0026thinsp;=\u0026thinsp;1.220, 95% CI: 1.094\u0026ndash;1.361, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, an increase in sleep time reduced the likelihood of developing chronic TMD by 0.826 times (OR\u0026thinsp;=\u0026thinsp;0.826, 95% CI: 0.733\u0026ndash;0.931, p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the multiple logistic regression analysis with backward selection, all previously mentioned factors were included simultaneously to determine their collective impact on the development of chronic TMD and the extent to which each factor contributed. Among these predictors, bruxism was identified as the most powerful predictor, increasing the likelihood of chronic TMD by 4.048 times (OR\u0026thinsp;=\u0026thinsp;4.048, 95% CI: 1.786\u0026ndash;9.173, p\u0026thinsp;=\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSingle and multiple regression analysis for predicting chronic TMD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eAnalysis 1: Single logistic regression analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAnalysis 2: Multiple logistic regression analysis with backward selection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDemographics\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUpper 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTMJ noise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0389*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBruxism\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep problem\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eADD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTMJ-OA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEffusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eJoint space narrowing\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTOP-Bang\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSTOP-Bang\u0026thinsp;\u0026ge;\u0026thinsp;5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eThe results were obtained using single and multiple regression analyses of chronic TMD. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. *: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***: p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. TMD, temporomandibular disorder; VAS, visual analog scale; TMJ, temporomandibular joint; ADD, anterior disc displacement; TMJ-OA, osteoarthritis of the TMJ; STOP-Bang, snoring, tiredness, observed apnea, high blood pressure (STOP)-BMI, age, neck circumference, and sex (Bang); SD, standard deviation; OR, odds ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*: Variables with an absolute weight value of 0.4 or higher are marked with an asterisk. The weight values range from \u0026minus;\u0026thinsp;1 to +\u0026thinsp;1, where the absolute value of the weight indicates its predictive power. A weight value closer to zero suggests lower predictive strength, indicating that the corresponding variable has little impact on the model's prediction. In contrast, a weight value closer to 1 (or -1) indicates a higher predictive strength, signifying that the variable plays a more significant role in the prediction process. A positive weight implied a direct relationship, whereas a negative weight indicated an inverse relationship.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e6) Cut-off value for predicting chronic TMD\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the cut-off values for sleep time, VAS, and STOP-Bang total scores in predicting chronic TMD. Among these, sleep time showed the strongest predictive power, with a cut-off value of 6.750 h (area under the curve [AUC]\u0026thinsp;=\u0026thinsp;0.614, 95% confidence interval [CI]: 0.543\u0026ndash;0.685, p\u0026thinsp;=\u0026thinsp;0.002). This suggests that individuals who sleep less than 6.750 hours are more likely to develop chronic TMD. Similarly, the VAS score was also a significant predictor of chronic TMD, with a cut-off value of 4.5 (AUC\u0026thinsp;=\u0026thinsp;0.647, 95% CI: 0.567\u0026ndash;0.707, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A VAS score higher than 4.5 indicates a significantly increased risk of developing chronic TMD. The STOP-Bang total score had a cut-off value of 2.50 (AUC\u0026thinsp;=\u0026thinsp;0.594, 95% CI: 0.522\u0026ndash;0.667, p\u0026thinsp;=\u0026thinsp;0.012), with scores above this threshold significantly increasing the likelihood of chronic TMD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e7) Correlations between the clinical characteristics and MRI findings\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhen analyzing the entire dataset, the chronicity of TMD symptoms was associated with ADD (r\u0026thinsp;=\u0026thinsp;0.30), joint space narrowing (r\u0026thinsp;=\u0026thinsp;0.29), TMJ OA (r\u0026thinsp;=\u0026thinsp;0.26), and effusion (r\u0026thinsp;=\u0026thinsp;0.09). Additionally, symptom chronicity was associated with clinical factors such as bruxism (r\u0026thinsp;=\u0026thinsp;0.19), TMJ noise (r\u0026thinsp;=\u0026thinsp;0.19), and TMD pain (r\u0026thinsp;=\u0026thinsp;0.01). The factors that most correlated with symptom chronicity were ADD (r\u0026thinsp;=\u0026thinsp;0.30), joint space narrowing (r\u0026thinsp;=\u0026thinsp;0.29), TMJ OA (r\u0026thinsp;=\u0026thinsp;0.26), TMJ noise (r\u0026thinsp;=\u0026thinsp;0.19), bruxism (r\u0026thinsp;=\u0026thinsp;0.19), effusion (r\u0026thinsp;=\u0026thinsp;0.09), and TMD pain (r\u0026thinsp;=\u0026thinsp;0.01). Furthermore, ADD exhibited the strongest correlation with joint space narrowing (r\u0026thinsp;=\u0026thinsp;0.82), along with positive correlations with TMJ-OA (r\u0026thinsp;=\u0026thinsp;0.24) and effusion (r\u0026thinsp;=\u0026thinsp;0.17). TMJ-OA showed a significant positive correlation with ADD (r\u0026thinsp;=\u0026thinsp;0.24), effusion (r\u0026thinsp;=\u0026thinsp;0.20), and joint space narrowing (r\u0026thinsp;=\u0026thinsp;0.20). Effusion was also correlated with ADD (r\u0026thinsp;=\u0026thinsp;0.17), TMJ-OA (r\u0026thinsp;=\u0026thinsp;0.20), and joint space narrowing (r\u0026thinsp;=\u0026thinsp;0.12).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eA similar pattern of correlations was observed when focusing on acute TMD, consistent with the findings from the entire dataset. However, a stronger relationship was observed between ADD and joint space narrowing (r\u0026thinsp;=\u0026thinsp;0.86). The correlation between effusion and ADD was also stronger than that observed in the whole dataset (r\u0026thinsp;=\u0026thinsp;0.21), whereas the relationship between TMJ OA and ADD was slightly weaker (r\u0026thinsp;=\u0026thinsp;0.20) compared to the whole dataset.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFor chronic TMD, the correlation between TMJ-OA and effusion was stronger than that for the entire dataset (r\u0026thinsp;=\u0026thinsp;0.27). However, the interrelationships among ADD, effusion, and joint space narrowing were weaker in chronic TMD than in the entire dataset and acute TMD.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e8) 2D and 3D visualization of interrelationships\u003c/h3\u003e\n\u003cp\u003eIn the 2D visualization, factors significantly associated with chronic TMD (Euclidean distance\u0026thinsp;\u0026le;\u0026thinsp;0.2) were identified. Among the MRI findings, the key factors were ADD, TMJ-OA, and joint space narrowing. For clinical characteristics, TMJ noise, bruxism, VAS, and sleep time were significantly associated with chronic TMD, with a STOP-Bang total score\u0026thinsp;\u0026ge;\u0026thinsp;5, also approaching a Euclidean distance of 0.2.\u003c/p\u003e \u003cp\u003eA 3D network was used to visualize the relationships among variables associated with chronic TMD. This visualization depicts the degree of interconnection between the factors associated with chronic TMD and their relationships with other variables. Among the clinical characteristics linked to chronic TMD, TMJ noise and bruxism are interrelated. Additionally, the VAS score was related to chronic TMD and was associated with effusion and sleep problems. MRI findings associated with chronic TMD include ADD, joint space narrowing of the TMJ, and TMJ-OA. The ADD demonstrated a strong relationship with joint space narrowing.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e9) Machine learning algorithms for chronic TMD\u003c/h2\u003e \u003cp\u003eWe employed logistic regression model within machine learning to identify significant predictors of chronic TMD and extracted the weight values for each factor, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This figure shows the variables useful for predicting chronic TMD and highlights their respective weight values. Significant positive predictors, defined by an absolute weight value of 0.4 or higher, were identified in the following order: bruxism (0.6768), VAS (0.5812), sleep problem (0.5230), joint space narrowing (0.5116), TMJ noise (0.4457), ADD (0.4337), and STOP-Bang\u0026thinsp;\u0026ge;\u0026thinsp;5 (0.4082). Thus, the presence or increase in these factors is associated with a higher likelihood of chronic TMD. Conversely, sleep duration was the only significant negative predictor. As sleep time decreases (weight = -0.5926), the likelihood of developing chronic TMD increases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e10) Comparison of prediction performance between machine learning and deep learning\u003c/h2\u003e \u003cp\u003eLogistic regression, a commonly used machine learning model, was employed to predict chronic TMD based on 19 clinical characteristics and MRI findings. The model achieved an AUROC of 0.7550 (95% confidence interval [CI], 0.6550\u0026ndash;0.8550). The prediction accuracy for chronic TMD was 0.7083, with higher sensitivity (73.4%) compared to specificity (68.4%). When using Multi-Layer Perceptron (MLP), a type of deep learning model, to predict chronic TMD, the AUROC improved to 0.7949 (95% CI: 0.6949\u0026ndash;0.8949). However, the difference in the AUROC between logistic regression and MLP was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.3067). In other words, the prediction performance of deep learning (MLP) was 3.99% higher than that of logistic regression (79.49% vs. 75.50%); however, the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.3067). The MLP model achieved a diagnostic accuracy of 77.08% for chronic TMD compared to acute TMD. Similar to the logistic regression, MLP demonstrated higher sensitivity (79.3%) than specificity (73.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides valuable insights into the distinguishing characteristics of acute and chronic TMD, focusing on clinical, sleep-related, and MRI-based predictors of chronicity. Our findings revealed that clinical and behavioral factors, including TMJ noise, bruxism, sleep problems, and elevated STOP-B Bang scores, were more prevalent in patients with chronic TMD than in those with acute TMD. Furthermore, structural abnormalities detected using MRI, such as anterior disc displacement (ADD), TMJ osteoarthritis, and joint space narrowing, highlight the relevance of joint-related changes in the chronicity of TMD symptoms. These findings underscore the multifaceted nature of chronic TMD and emphasize the importance of early intervention to prevent symptom persistence and deterioration. Identifying these key predictors will help clinicians proactively manage TMD and improve long-term patient outcomes.\u003c/p\u003e \u003cp\u003ePain, sleep disturbance, and chronic symptoms are closely interconnected \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. This study confirmed that patients with chronic TMD experience greater subjective pain, as reflected by higher VAS scores and shorter sleep durations, compared to those with acute TMD. These findings highlight that high pain intensity is significantly associated with chronic TMD, and sleep duration is notably shorter in patients with chronic TMD than in those with acute symptoms. These behavioral patterns suggest that chronic pain is closely linked to sleep disturbances, which is consistent with prior research showing that poor sleep quality exacerbates pain perception and impairs recovery from musculoskeletal conditions \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Elevated STOP-Bang scores in patients with chronic TMD further underscore the need to address sleep-related breathing disorders such as OSA as part of TMD management strategies. Although a complete consensus has not been reached, OSA is considered a potential risk factor for TMD \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Emerging studies have suggested that OSA and TMD share several overlapping mechanisms\u0026mdash;such as sleep disturbances, bruxism, and inflammatory responses, which may contribute to the development or exacerbation of TMD symptoms \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Both conditions can disrupt normal sleep patterns, leading to increased pain sensitivity and psychological distress, which further complicates clinical outcomes \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In this study, machine learning revealed that a high risk of OSA, as indicated by a high STOP-Bang total score, is a significant predictor of chronic TMD. As research continues to explore this relationship, identifying OSA as a risk factor for TMD holds promise for integrated therapeutic approaches targeting both conditions. These findings are consistent with those of previous studies, suggesting that early intervention targeting joint health may prevent long-term damage and improve clinical outcomes.\u003c/p\u003e \u003cp\u003eThe MRI findings indicate that structural changes in the TMJ play a significant role in the transition from acute to chronic TMD. The higher prevalence of ADD, TMJ OA, and joint space narrowing in patients with chronic TMD supports the hypothesis that prolonged TMD contributes to joint deterioration and persistent symptom. TMJ noise is common in acute TMD and may be linked to ADD \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. ADD plays a pivotal role in the progression of chronic TMD by initiating structural changes \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. As ADD develops, it may gradually lead to joint space narrowing due to increased friction between the mandibular condyle, temporal bone, or articular disc \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This friction can further contribute to the development of TMJ-OA over time. In contrast, effusion\u0026mdash;a fluid accumulation within the TMJ\u0026mdash;was observed in over 60% of patients with TMD, with a prevalence of 62.4% in acute TMD and 71.3% in chronic TMD; however, effusion did not emerge as a distinguishing predictor of chronic TMD. Despite its relatively high frequency, effusion demonstrates a weak association with other MRI abnormalities. This suggests that, while effusion might reflect an inflammatory response, it is not a reliable indicator of the transition from acute to chronic TMD or the presence of other structural changes such as ADD, joint space narrowing, or TMJ OA.\u003c/p\u003e \u003cp\u003eFrom a diagnostic perspective, both the logistic regression and deep learning models exhibited high diagnostic performance in predicting chronic TMD, demonstrating their potential utility in clinical practice. This finding suggests that while advanced AI models, such as deep learning, offer slight predictive advantages by identifying complex patterns in data, simpler machine learning models, such as logistic regression, remain valuable for clinical applications \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Their strength lies in their ease of interpretation, which is crucial for clinicians making both time-sensitive and time-consuming decisions \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. These tools can enhance patient outcomes by minimizing diagnostic errors and improving decision-making efficiency, thus enhancing patient outcomes. Moreover, the transparency and ease of interpretation of logistic regression make it a practical and reliable option for scenarios where clinical understanding and rapid insight are critical \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. As deep learning methods continue to advance and gain wider acceptance in healthcare \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, traditional machine learning models, such as logistic regression, remain essential, offering a balance between predictive power and clinical usability. In addition to general statistics, our attempt at this time includes 2D and 3D visualization as well as AI-based analysis, which can significantly help in understanding the complex interrelationships associated with chronic TMD.\u003c/p\u003e \u003cp\u003eBruxism, characterized by the involuntary clenching or grinding of teeth, is a significant contributing factor to the development and persistence of chronic TMD \u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. This condition can occur during sleep (sleep bruxism) or while awake (awake bruxism), and both forms are associated with excessive loading of the TMJ and masticatory muscles \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Repetitive mechanical stress from bruxism can exacerbate joint wear and tear, increasing the likelihood of structural abnormalities such as ADD and TMJ OA \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. These changes strongly correlate with symptom chronicity, as confirmed by the higher prevalence of both bruxism and MRI abnormalities in patients with chronic TMD than in those with acute TMD. Behaviorally, bruxism can disrupt sleep, contributing to poor sleep quality and increased pain sensitivity, further complicating TMD management \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. This is consistent with the findings of the present study, in which patients with chronic TMD reported a higher frequency of bruxism and shorter sleep duration. Addressing bruxism through targeted interventions, such as behavioral therapy, occlusal appliances, or stress management, may help reduce joint strain, improve sleep quality \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and ultimately prevent the progression from acute to chronic TMD. This emphasizes the need for clinicians to proactively evaluate and manage bruxism in patients proactively, given its pivotal role in the persistence and progression of symptoms.\u003c/p\u003e \u003cp\u003eThis study suggests that incorporating clinical symptoms, including bruxism, sleep patterns, and structural abnormalities, into treatment strategies may help mitigate the progression to chronic TMD. However, this study has certain limitations. First, the study retrospective design, may have introduced selection bias and limited the ability to establish causal relationships between the predictors and chronic TMD. Second, the sample size, although calculated to achieve adequate statistical power, was drawn from a single institution, potentially affecting the generalizability of the findings to a broader population. Future studies are needed to validate these results across multiple institutions and diverse populations. Third, while the STOP-Bang questionnaire was used to assess OSA risk, polysomnographic evaluation provided more accurate data on sleep disturbance. Fourth, although the study incorporated advanced AI models, the performance of the deep learning model was only marginally better than that of logistic regression, suggesting that further optimization and the inclusion of more comprehensive datasets may be required to enhance predictive accuracy. Finally, behavioral and psychological factors were assessed based on patient self-reports, which are subject to recall bias and may affect the reliability of the data. Future studies should consider longitudinal designs and include objective behavioral assessments to provide more robust evidence regarding the predictors of chronic TMD.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrated that the progression from acute to chronic TMD is influenced by a combination of clinical, behavioral, and structural factors. TMJ noise, bruxism, high VAS scores, sleep disturbances, and specific MRI abnormalities were significant predictors of chronic TMD. AI-based models, particularly deep learning, enhance the explanatory power of these predictors and aid in identifying patients at risk of chronicity. Although deep learning offers slight improvements in predictive performance, traditional machine learning models remain valuable because of their interpretability and ease of use. Clinicians can leverage these findings to develop personalized treatment strategies, emphasize early intervention to prevent symptom chronicity, improve clinical outcomes, and enhance the quality of life in patients with TMD.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003e The research protocol for this study was reviewed to ensure compliance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Kyung Hee University Dental Hospital in Seoul, South Korea (KHD IRB, IRB No-KH-DT24025). Informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThe study population comprised 239 consecutive patients with TMD (161 women and 78 men; mean age 35.60\u0026thinsp;\u0026plusmn;\u0026thinsp;17.93 years) who visited Kyung Hee University Dental Hospital between January 2020 and September 2024. All diagnoses were made by a specialist with more than 10 years of clinical experience following the diagnostic criteria for TMD Axis I \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Patients with TMD were identified, and all clinical reports and MRI images of the TMJs were retrospectively reviewed. The duration of TMD symptoms, as reported by the patients, was recorded in months, with 6 months used as the threshold to classify the patients into two groups: acute TMD (symptom duration\u0026thinsp;\u0026lt;\u0026thinsp;6 months) and chronic TMD (symptom duration\u0026thinsp;\u0026ge;\u0026thinsp;6 months) \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This study compared the clinical and MRI characteristics associated with chronic TMD to those observed in acute TMD and examined factors contributing to the prolonged duration of symptoms. The exclusion criteria were history of severe injuries, such as unstable multiple trauma to the orofacial area and maxillary and mandibular fractures; systemic diseases potentially affecting the TMJ, such as rheumatic diseases, systemic osteoarthritis, pregnancy, psychological problems, psychiatric or neurological disorders; and cases in which the structure of the TMJ complex was not clearly distinguishable on MRI \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSample size\u003c/h2\u003e \u003cp\u003eThe sample size was calculated using G*Power version 3.1.9.7 (Heinrich-Heine-Universit\u0026auml;t D\u0026uuml;sseldorf, D\u0026uuml;sseldorf, Germany). A total of 134 participants (alpha error, 0.05; actual power, 0.95) were included in the target sample, and 239 patients with TMD were recruited.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePain intensity\u003c/h2\u003e \u003cp\u003ePain intensity was assessed using the visual analog scale (VAS). The VAS score ranged from 0 to 10, with 0 indicating no pain and 10 indicating the worst imaginable pain.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClinical symptoms and contributing factors for TMD\u003c/h2\u003e \u003cp\u003eSix clinical symptoms were investigated in the patients with TMD: TMJ noise, TMD pain, locking, muscle stiffness, bruxism, and tinnitus. The presence of each parameter was recorded based on patient reports and assessed dichotomously as either \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no.\u0026rdquo;. The criteria for each significant complaint were as follows: (1) TMJ noise: sounds such as clicking and crepitus originating from the TMJ during both functional and nonfunctional movement of the mandible, (2) TMD pain: pain associated with TMD involving the TMJ structures, (3) Locking: jaw locking with a mouth opening of \u0026lt;\u0026thinsp;35 mm, indicating limited mouth opening, as reported by the patient, (4) Muscle stiffness: stiffness, heaviness, or discomfort in muscles during function or at rest, (5) Bruxism: clenching or grinding of teeth while awake or sleeping, and (6) Tinnitus: A perception of various sounds, such as ringing or buzzing, without any corresponding external source. The three contributing factors included sleep disturbance, psychological stress, and a history of macro-trauma, with each factor assessed in a dichotomous manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSTOP-Bang and sleep time\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe STOP-Bang questionnaire was used to evaluate factors associated obstructive sleep apnea (OSA). This validated screening tool is designed to identify individuals with a high likelihood of OSA. The STOP-Bang questionnaire consists of eight dichotomous (yes/no) questions related to the clinical features of sleep apnea. Each question, a response \u0026ldquo;yes\u0026rdquo; scores 1, a \u0026ldquo;no\u0026rdquo; response scores 0, and the total score ranges from 0 to 8. The exposure of interest was classified as either binary-low or high likelihood for OSA; the low likelihood of OSA: Yes to \u0026lt;\u0026thinsp;3 questions, moderate likelihood of OSA: Yes to \u0026ge;\u0026thinsp;3 questions, and high likelihood of OSA: Yes to \u0026ge;\u0026thinsp;5 questions \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. All patients were instructed to complete the STOP-BANG questionnaire. Additionally, we collected the average self-reported sleep time over the past two weeks in hours.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMR image acquisition\u003c/h2\u003e \u003cp\u003eHigh-resolution MRIs were obtained using a 3T MRI system (Signa\u0026trade; Genesis, GE Healthcare, Chicago, IL, USA) with a 6-cm \u0026times; 8-cm diameter surface coil. The MRI examinations were performed using the MR sequences and protocols of the Kyung Hee University Medical Center. All scans involved sagittal oblique sections (section thickness, \u0026le;\u0026thinsp;3 mm; field of view, 15 cm; matrix dimensions, 256 \u0026times; 224), and spin-echo sagittal MRIs were obtained on axial localizer images. T2-weighted images (T2WIs) were obtained using a 650/14 repetition time (TR)/echo time (TE) and 2650/82 TR/TE sequences. Proton density (PD) images were obtained using a 2650/82 TR/TE sequence. MRI protocols for TMJ evaluation were performed as described previously \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eMRI abnormal findings\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eADD, TMJ-OA, joint space narrowing, and effusion were coded dichotomously as positive or negative. The left and right sides of the patients with bilateral TMJ and ADD were evaluated separately using T2-weighted (T2WI) and proton density (PD) images. If an abnormal finding was present on either side, it was recorded as 'positive. MRI indicators for assessing ADD in patients with TMD were defined as follows \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e: a positive finding of ADD was identified when the posterior band of the articular disc was displaced anteriorly beyond the normal range in the closed-mouth position. A positive finding of TMJ-OA was indicated by the presence of cortical erosion, subchondral cysts, osteophyte formation, flattening, or sclerosis, either alone or in combination \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, the presence of flattening and sclerosis alone is insufficient to diagnose TMJ-OA. Joint space narrowing was defined as a distance of less than 1.5 mm between the outer lines of the condyle and temporal bone \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A positive finding of TMJ effusion was recorded when a high-intensity signal was observed in the superior or inferior joint space on closed-mouth sagittal T2WI or PD-weighted images \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Intra-examiner reproducibility yielded intra-class correlation coefficients (ICCs) of 0.78 and 0.85, while inter-examiner ICCs for TMJ effusion diagnosis were 0.79 and 0.83, respectively. Disagreements were resolved through discussion until a consensus was reached.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eAI-based evaluation and visualization\u003c/h2\u003e \u003cp\u003eThe neural network model used in this study follows a multi-layer perceptron (MLP) architecture designed to perform a binary classification of chronic TMD against acute TMD. The dataset was divided into 60% training, 20% validation, and 20% for testing. The input layer received clinical data (features), which were standardized to ensure that the mean of all input features was zero, and the standard deviation was 1. This was followed by four hidden layers that were sequentially connected, with each layer consisting of 128, 64, 32, and 16 neurons, respectively, gradually decreasing in size. Each hidden layer applies the ReLU activation function to introduce nonlinearity, thereby enhancing the model\u0026rsquo;s ability to learn complex patterns. To prevent overfitting, a dropout rate of 30% was applied to each hidden layer. The output layer consisted of two output nodes for binary classification. CrossEntropyLoss was used as the loss function, and softmax was applied to perform the classification. The activation function was applied in the final output layer. The model was trained for 100 epochs, and the training loss, validation loss, and accuracy were recorded for each epoch. The Adam optimizer was employed due to its ability to handle sparse gradients and noisy data efficiently, as well as its adaptive learning rate, which allows for faster convergence compared to standard stochastic gradient descent. The optimizer combines the advantages of momentum and RMSProp, making it particularly effective for deep-learning tasks. The model with the best performance, defined as the one with the lowest validation loss, was selected and evaluated using the test set to determine its final accuracy. The performance of the MLP deep learning model was compared to that of traditional machine learning methods to assess its effectiveness in predicting chronic TMD. The diagnostic performance of the deep learning model was compared to that of conventional machine learning models (logistic regression model). To advance beyond an isolated understanding of the relationships between variables, we aimed to provide a comprehensive and intuitive understanding through 2D (two-dimensional) and 3D (three-dimensional) visualizations of the relationships between chronic TMD and related variables. During training, the model stored the weights at the point at which the validation loss was minimized, thereby ensuring optimal performance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eThe code for the deep learning algorithm developed in this study for predicting chronic TMD is available on GitHub at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/SeonggwangJeon/Chronic_VAS_visualization/tree/main\u003c/span\u003e\u003cspan address=\"https://github.com/SeonggwangJeon/Chronic_VAS_visualization/tree/main\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eData were analyzed using IBM SPSS Statistics for Windows (version 26.0; IBM Corp., Armonk, NY, USA). Descriptive statistics are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or frequencies with percentages, as appropriate. The distributions of categorical data were assessed using the χ\u0026sup2; test with Bonferroni correction for equality of proportions. Student\u0026rsquo;s t-tests were used to compare the mean values between the two TMD groups. Cramer's V analysis was also used to assess the strength of the associations between the two variables; the statistical values ranged from 0 to 1, with values closer to 1 indicating a stronger correlation. To predict chronic TMD compared to acute TMD, we identified variables with significant differences in means or percentages between acute and chronic TMD groups using t-tests, χ\u0026sup2; tests, and Bonferroni correction. The contribution of each selected variable was further evaluated using a single logistic regression analysis, with the results expressed as odds ratios (ORs) and 95% confidence intervals (CIs). To assess the combined predictive power of these factors for chronic TMD, we performed multiple logistic regression analysis with backward selection. All analyses were considered statistically significant at a two-tailed p-value of \u0026lt;\u0026thinsp;0.05. The performance of the prediction model for chronic TMD was evaluated by plotting the receiver operating characteristic (ROC) curve against the area under the ROC curve (AUC) calculated for each AI model. AUC values were interpreted as follows: AUC\u0026thinsp;=\u0026thinsp;0.5 (no discrimination), 0.6\u0026thinsp;\u0026ge;\u0026thinsp;AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.5 (poor discrimination), 0.7\u0026thinsp;\u0026ge;\u0026thinsp;AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.6 (acceptable discrimination), 0.8\u0026thinsp;\u0026ge;\u0026thinsp;AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7 (excellent discrimination); and AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9 (outstanding discrimination) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend their special thanks to\u0026nbsp;Sung-Woo Lee of the Department of Oral Medicine and Oral Diagnosis at Seoul National University and to\u0026nbsp;Jung-Pyo Hong of the Department of Orofacial Pain and Oral Medicine at Kyung Hee University Dental Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all patients prior to participation in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting and original draft preparation, Y-HL; conceptualization, Y-HL; methodology, Y-HL; software, Y-HL; validation and formal analysis, Y-HL, Q-SA, and J-HL; investigation, Y-HL and SJ; resources, Y-HL and Q-SA; data curation, Y-HL; writing, review, and editing, Y-HL; visualization, Y-HL; supervision, Y-HL and Y-KN; project administration, Y-HL and Y-KN; and funding acquisition, Y-HL. All the authors contributed to and approved the submission of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research protocol complied with the Declaration of Helsinki and was approved by the Institutional Review Board of Kyung Hee University Dental Hospital in Seoul, South Korea (IRB No-KH-\u0026nbsp;No-KH-DT24025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2020R1F1A1070072, No. RS-2024-0042120), IITP/MSIT (IITP-2021-0-02068, RS-2020-II201373, RS-2023-00220628), and Kyung Hee University in 2021 (KHU-20211863).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePalmer J, Durham J (2021) Temporomandibular disorders. 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Jpn Dent Sci Rev 58:124\u0026ndash;136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jdsr.2022.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.jdsr.2022.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"temporomandibular disorder, chronic, disc displacement, machine learning, deep learning, magnetic resonance imaging","lastPublishedDoi":"10.21203/rs.3.rs-5336211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5336211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to identify factors that significantly contribute to the chronicity of symptoms in patients with temporomandibular disorders (TMD). Statistical, machine learning, and deep learning models were used for analysis. The study include 239 patients with TMD (161 women and 78 men; mean age 35.60\u0026thinsp;\u0026plusmn;\u0026thinsp;17.93 years), diagnosed using Diagnostic Criteria for TMD (Axis I). Participants were categorized into: acute TMD (\u0026lt;\u0026thinsp;6 months) and chronic TMD (\u0026ge;\u0026thinsp;6 months) (51.05%). Significance clinical findings revealed that temporomandibular joint (TMJ) noise and bruxism were more frequently reported in patients with chronic TMD than in those with acute TMD. The visual analog scale (VAS) score, reflecting subjective pain intensity, was significantly higher in chronic TMD than in acute TMD. Additionally, patients with chronic TMD had shorter average sleep durations than their acute counterparts. STOP-Bang total scores were higher in chronic TMD (3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09 vs. 2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73, p\u0026thinsp;=\u0026thinsp;0.012) than in acute TMD. Magnetic resonance imaging revealed structural abnormalities in patients with chronic TMD, with significantly higher rates of anterior disc displacement (ADD), TMJ osteoarthritis, and joint space narrowing. Using logistic regression\u0026mdash;a widely recognized machine learning model\u0026mdash;the AUROC for predicting chronic TMD was 0.7550 (95% CI]: 0.6550\u0026ndash;0.8550). Significant predictors of chronic TMD included TMJ noise, bruxism, VAS score, sleep disturbance, STOP-Bang total score\u0026thinsp;\u0026ge;\u0026thinsp;5 (high risk of obstructive sleep apnea), ADD, and joint space narrowing. Deep learning (multilayered perceptron) improved prediction performance by 3.99% over logistic regression (79.49% vs. 75.50%, p\u0026thinsp;=\u0026thinsp;0.3067). These findings may help clinicians prevent symptom chronicity and mitigate the progression to chronic TMD, ultimately improving patient care.\u003c/p\u003e","manuscriptTitle":"Clinical and MRI Markers for Acute vs Chronic TMD Using a Machine Learning and Deep Learning Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 18:11:09","doi":"10.21203/rs.3.rs-5336211/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmed","sideBox":"Learn more about [Communications Medicine](http://www.nature.com/commsmed)","snPcode":"43856","submissionUrl":"https://mts-commsmed.nature.com/cgi-bin/main.plex","title":"Communications Medicine","twitterHandle":"@commsmedicine","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9b59dd0-46b0-4042-a86e-696567f6a997","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":39923591,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":39923592,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":39923593,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-09-30T07:09:42+00:00","versionOfRecord":{"articleIdentity":"rs-5336211","link":"https://doi.org/10.1038/s43856-025-01081-5","journal":{"identity":"communications-medicine","isVorOnly":false,"title":"Communications Medicine"},"publishedOn":"2025-09-29 04:00:00","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2024-12-18 18:11:09","video":"","vorDoi":"10.1038/s43856-025-01081-5","vorDoiUrl":"https://doi.org/10.1038/s43856-025-01081-5","workflowStages":[]},"version":"v1","identity":"rs-5336211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5336211","identity":"rs-5336211","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}