Construction and evaluation of a model based on clinical factors and multimodal ultrasound parameters for diagnosing postpartum pelvic floor myofascial pain.

Pelvic floor myofascial pain (PFMP) originates from the pelvic floor muscles (PFMs) and associated fascial structures and is frequently accompanied by notable emotional disturbances and pelvic floor dysfunction. This condition is a nonnegligible cause of chronic pelvic pain (CPP), with 22–94% of CPP cases being associated with PFMP ( 1 ). Epidemiological studies show that pregnancy and childbirth are independent risk factors for postpartum pelvic organ dysfunction ( 2 ). PFMP is common among postpartum women, with an incidence of approximately 19% ( 3 , 4 ). The primary manifestation of PFMP is excessive spasm of the PFM, characterized by the presence of highly sensitive myofascial trigger points within taut muscle bands that manifest as tender nodules ( 5 , 6 ). The management of PFMP requires a multidisciplinary approach ( 7 ), as PFMP presents with a constellation of symptoms and nonspecific clinical manifestations. These factors often delay diagnosis and treatment, which may lead to progression to CPP, and adversely affect physical and mental health ( 8 ). At present, the clinical diagnosis of PFMP primarily relies on standardised palpation of the PFM through vaginal or rectal examination ( 9 ). Despite the high incidence of PFMP, clinicians do not routinely conduct standardised PFM palpation during physical examinations ( 10 ) due to several factors. First, the complexity of pelvic organs and muscle connective tissues involves multiple disciplines, which often results in patient referrals across departments, including gynecology, urology, and proctology. Unless they are specialized pelvic floor physical therapists, outpatient clinicians tend to neglect PFM examinations due to an insufficiency in theoretical knowledge and skills regarding the management of pelvic floor disorders. Moreover, when PFM examinations are conducted, they often lack standardisation ( 11 ). Second, vaginal or rectal examinations are invasive, leading to resistance from some patients. Indeed, it has been speculated that the misdiagnosis and underdiagnosis of PFMP is significant and that its actual incidence may be higher than that reported in the literature ( 12 ). Therefore, there is a critical need to identify non-invasive and objective imaging biomarkers for the diagnosis of PFMP. There is currently no consensus on an auxiliary examination method for PFMP ( 9 ). Recent systematic reviews on alternative diagnostic approaches for PFMP have revealed a scarcity of high-quality evidence. Specifically, an analysis of the related literature over the past five decades identified only five studies meeting the methodological criteria, with a notable absence of high-quality research specifically addressing the diagnostic accuracy of PFMP assessment methods ( 13 ). The diagnostic approaches for PFMP remain problematic: standardised vaginal palpation has inadequate sensitivity, while alternative tools such as the Central Sensitization Inventory exhibit suboptimal diagnostic specificity ( 14 ). This diagnostic challenge is particularly pronounced in postpartum PFMP, a condition characterized by unique physiological adaptations of the pelvic floor and multifactorial pain mechanisms that involve both structural and neuroregulatory pathologies. This complexity necessitates the development of integrated assessment strategies that combine clinical profiling with quantitative imaging biomarkers. Shear wave elastography (SWE), an emerging imaging modality, has demonstrated excellent performance in evaluating the biomechanical properties of skeletal muscles ( 15 , 16 ). The application of SWE in diagnosing myofascial pain syndromes has recently garnered heightened clinical interest. For instance, Valera-Calero et al. ( 17 ) reported that SWE provides significant advantages in detecting stiffness variations in myofascial trigger points, while Hao et al. ( 18 ) demonstrated its utility in the quantitatively assessment of trapezius muscle stiffness in patients with trapezius myofascial pain. However, no clinical studies thus far have evaluated the application of SWE in the diagnosis of PFMP. Pelvic floor ultrasound is a noninvasive diagnostic modality with high precision, providing crucial information on pelvic floor structure and function ( 19 ). For PFM, two-dimensional ultrasound (2DUS) is primarily used to examine organ positioning and PFM morphology, while four-dimensional ultrasound (4DUS) incorporates the temporal dimension to dynamically visualize muscular changes under varying conditions, enabling assessment of muscle tension ( 20 ). A randomized controlled trial conducted by Del Forno et al. ( 21 ) demonstrated that levator hiatus area (LHA) measurements obtained through 4DUS effectively reflect PFM tension in patients with endometriosis. Furthermore, clinical evaluation by Morin et al. ( 22 ) confirmed that measurements of the LHA and anorectal angle (ARA), along with their dynamic changes under different conditions, can serve as reliable indicators for the assessment of PFM tone. However, the diagnostic efficacy of pelvic floor ultrasound in the diagnosis of PFMP has yet to be clinically validated. Within this context, we conducted a study which represents the first systematic investigation into the association between multimodal pelvic floor ultrasound parameters and postpartum PFMP. Furthermore, we constructed a diagnostic model by combining these ultrasonographic measures with key clinical characteristics. The overall aim of this work is to establish an evidence-based foundation to inform the early screening of postpartum PFMP and the development of personalized intervention strategies. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-aw-2479/rc ).

This prospective, observational study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of Quanzhou First Hospital Affiliated to Fujian Medical University [No. Quanyi Ethics (2024) K118] and informed consent was obtained from all individual participants. Participants were categorized into PFMP and non-PFMP groups, with the PFMP group meeting the diagnostic criteria for PFMP as outlined by the International Continence Society in 2021 ( 9 ). The inclusion criteria were as follows: (I) women examination conducted at the 6-to-8-week postpartum interval; (II) standardised PFM palpation assessment; (III) pelvic floor ultrasonography and SWE of the PFM; and (IV) standardised palpation and ultrasound examinations conducted on the same day. Meanwhile, the exclusion criteria were as follows: (I) history of pelvic surgery; (II) acute urinary or pelvic infections; (III) history of neurological conditions such as lumbar disc disease or sciatica; (IV) grade-II-or-higher pelvic organ prolapse; (V) ineffective Valsalva maneuver (VM) during pelvic floor ultrasound; (VI) inadequate quality control of SWE; and (VII) spontaneous vaginal deliveries assisted by instruments (e.g., vacuum- or forceps-assisted) were excluded. Between January 2024 and October 2025, a total of 346 eligible participants were recruited from the postpartum screening clinic of a major hospital in southeastern China, according to the predefined inclusion and exclusion criteria. These participants were subsequently randomized into training and validation sets at a 7:3 ratio, resulting in 242 and 104 patients in the respective cohorts. The recruitment process is presented in Figure 1 . Flow chart of recruitment. PFMP, pelvic floor myofascial pain; SWE, shear wave elastography. The pelvic floor therapist conducted the PFM assessment following standardised palpation procedures ( 6 , 23 , 24 ). To ensure maximal consistency, all assessments were performed by a single senior pelvic floor therapist (X.L.) with over 10 years of experience in diagnosing pelvic floor dysfunction. The palpation was performed according to the predefined protocol based on the established guidelines ( 25 ), with the “clock face” method being applied for anatomical orientation. The participants were placed in a supine lithotomy position, and the examiner used the index finger to locate the PFM through palpation from superficial to deep layers along the 0–12 o’clock direction. Participants experiencing pain were included in the PFMP group, whereas those without pain were included in the non-PFMP group. Clinical data, including age, parity, mode of delivery, neonatal weight, predelivery body mass index (BMI), and weight gain during pregnancy were collected for all participants. As per the standardised anatomical nomenclature, the levator ani comprises the puborectalis muscle (PRM), pubococcygeus, and iliococcygeus muscles. Given the overlapping origins and continuous muscle fibers of the PRM and pubococcygeus muscles ( 26 ), our study delineated them as a unified sonographic structure, the pubovisceral muscle, on ultrasound imaging. Accordingly, for SWE imaging in our study, the levator ani was evaluated as two distinct regions: the pubovisceral muscle and the iliococcygeus muscle. An ACUSON Sequoia diagnostic ultrasound elastography diagnostic device (Siemens Healthineers, Erlangen, Germany) equipped with a 4- to 10-MHz linear probe was used for the examinations. Participants assumed a lithotomy position, and the probe was placed at the vaginal orifice to capture the standard sagittal plane of the pelvic floor and clearly visualize the ARA. The probe was tilted 10–30º outward from the midline of the body. Observations started at the U-shaped junction behind the ARA and proceeded to examine the PRM to the pubic symphysis attachment point ( 27 ). The probe was kept parallel to the muscle group to avoid muscle-bundle anisotropy ( 28 ). The musculoskeletal SWE mode was activated first, followed by the SWE quality test mode. Once the quality test results were deemed satisfactory, the region of interest (ROI) diameter was set to 3 mm, and shear wave velocity (SWV) measurements were captured at three points: the pubovisceral muscle attachment, muscle belly, and muscle tail. Based on the anatomical location of the iliococcygeus muscle ( 29 ), the probe was adjusted slightly backward and outward when the long axis of the pubovisceral muscle was being displayed. This adjustment allowed for the visualization of the iliococcygeus muscle, which is continuous but oriented differently from the pubovisceral muscle. SWV measurements of the iliococcygeus muscle were obtained at two points: near the attachment and at the middle of the muscle. Each measurement was repeated three times, and the average value was calculated. Measurements were conducted bilaterally at 10 points across the pelvic floor. The mean of these 10 measurements represented the average SWV (SWVmean) of the PFM ( Figure 2 ). Shear wave elasticity measurement sonography of pubovisceral (A) and iliococcygeal (B) muscle. A Voluson E8 colour Doppler ultrasound device (GE HealthCare, Chicago, IL, USA) with a 4- to 8-MHz volumetric probe was used. The pelvic floor operating system was applied with a maximum volume-sampling angle of 85° in the sagittal plane and 90° in the coronal plane ( 30 ). 2DUS images of the midsagittal plane of the pelvic floor and the 4DUS images of the hiatal plane were collected in the resting state and during the maximum effective VM. ARA, levator plate angle (LPA), LHA, and levator hiatus anteroposterior (LHAP) diameter were measured in both states ( Figure 3 ). Changes in the PFM during the VM were quantified as the ARA excursion on maximum VM, LPA excursion on maximum VM, percentage change in LHA, and percentage change in LHAP, which were calculated with following respective formulae: ❖ ARA excursion on maximum VM = ARA on maximum VM − ARA at rest; ❖ LPA excursion on maximum VM = LPA on maximum VM − LPA at rest; ❖ Percentage change in LHA = (LHA on maximum VM − LHA at rest)/LHA at rest; ❖ Percentage change in LHAP = (LHAP on maximum VM − LHAP at rest)/LHAP at rest. Measurement sonography of LPA and ARA at rest state (A), LPA and ARA at Valsalva state (B), LHAP and LHA of at rest (C) and Valsalva state (D). (A) At rest, LPA =31.6°, ARA =100.6°. (B) At Valsalva state, LPA =9.2°, ARA =112.8°. (C) At rest, LHA =10.12 cm 2 , LHAP =4.2 cm. (D) At Valsalva state, LHA =17.38 cm 2 , LHAP =5.6 cm. ARA, anorectal angle; LHA, levator hiatus area; LHAP, levator hiatus anteroposterior diameter; LPA, levator plate angle; SP, symphysis pubis. All transperineal ultrasound examinations, including 2DUS, 4DUS, and SWE imaging were performed by two senior sonographers (L.W. and X.W.) who had over 5 years of experience in pelvic floor ultrasonography and who were blinded to the participant clinical information. To evaluate intra-observer and inter-observer reliability, a random sample of 30 women was selected for reproducibility analysis. The primary sonographer (L.W.) conducted two separate measurements of all ultrasound parameters to assess intra-observer consistency. To evaluate inter-observer reliability, a second sonographer (X.W.) independently performed a separate set of measurements. Both sonographers conducted their assessments independently and were blinded to each other’s results to ensure objectivity. SPSS 27.0 (IBM Corp., Armonk, NY, USA) and R version 4.2.2 (The R Foundation for Statistical Computing, Vienna, Austria) were used for statistical analyses. Normally distributed data are expressed as the mean ± standard deviation, whereas non-normally distributed data are presented as the median and interquartile range. Meanwhile, categorical data are presented as rates or composition ratios. Group comparisons of continuous variables were conducted via the t-test or Mann-Whitney U test, and categorical variables were compared via the χ 2 test. Statistical significance was set at a P value <0.05. Missing data were handled through complete-case analysis, with only participants with full records on all predictors and the outcome being included in the model development and validation. Statistically significant variables (P<0.05) were screened out via univariate logistic regression analysis and the backward multivariate regression method. Multiple linear correlation tests were conducted by the variance inflation factor (VIF). Subsequently, variables that were significantly associated with PFMP in the multivariate analysis were selected for inclusion in the final model. For the validation cohort, predicted probabilities were computed by applying the regression coefficients from the final logistic regression model developed in the training set. Specifically, each participant’s clinical and ultrasonographic data were incorporated into the model equation to generate individual predictions, without any recalibration or modification of the original model coefficients. The nomogram model was validated through 1,000 bootstrap resamples. The performance of the model was evaluated according to the receiver operating characteristic (ROC) curve, calibration curve, Hosmer-Lemeshow (HL) test, and decision curve analysis (DCA). The sample size was determined based on the events per variable (EPV) criterion for logistic regression. With four predictor candidates anticipated in the final model, and an estimated PFMP incidence of 19% in the postpartum population, a minimum of 40 outcome events was deemed necessary (10 EPV × 4 predictors). To achieve this, a total sample size of approximately 211 participants was required (40 events/0.19). Our final enrolment of 346 participants exceeded this requirement, and thus adequate statistical power for model development was ensured.

Ultimately, 346 participants were recruited, including 166 in the PFMP group (48%) and 180 in the non-PFMP group (52%). The age ranged from 28 to 33 years, with an average age of 31 years. The incidence of PFMP was evenly distributed across the two sets, at 49% and 46% in the training and validation sets, respectively (P=0.743). The clinical characteristics and ultrasound features were not significantly different between the two sets ( Table 1 ). Data are presented as n (%), median [interquartile range], or mean ± standard deviation. ARA, anorectal angle; BMI, body mass index; LHA, levator hiatus area; LHAP, levator hiatus anteroposterior; LPA, levator plate angle; PFMP, pelvic floor myofascial pain; SWV, shear wave velocity; VM, Valsalva maneuver. The consistency for intra- and inter-observer measurements were good, with the intraclass correlation coefficient (ICC) value exceeding 0.75 for the ARA, LPA, LHAP, LHA, and SWVmean ( Table 2 ). CI, confidence interval; ICC, intraclass correlation coefficient. Table 3 summarizes the univariate and multivariate logistic regression analyses for postpartum PFMP. Univariate logistic regression analysis indicated that predelivery BMI, weight gain during pregnancy, ARA on maximum VM, LHA on maximum VM, ARA excursion on maximum VM, percentage change in LHA, and SWVmean were factors associated with postpartum PFMP. After the multivariate logistic regression analysis, weight gain during pregnancy, ARA excursion on maximum VM, percentage change in LHA, and SWVmean remained independently associated with postpartum PFMP. Collinearity was tested among these independent factors via the VIF, and all VIF values were <5, indicating no multicollinearity. ARA, anorectal angle; BMI, body mass index; CI, confidence interval; LHA, levator hiatus area; LHAP, levator hiatus anteroposterior; LPA, levator plate angle; OR, odds ratio; SWV, shear wave velocity; VM, Valsalva maneuver. The selection of variables for the final model selection was performed through use of a backward step-down selection process with the Akaike information criterion. Based on the binary logistic regression analysis, the regression equation for the model was as follows ( Table 4 ): Logit(P) = −6.681 + (0.273 × weight gain during pregnancy) + (−0.145 × ARA excursion on maximum VM) + (−0.941 × percentage change in LHA) + (0.942 × SWVmean). Four independent factors were incorporated into the model as diagnostic variables ( Figure 4 ). ARA, anorectal angle; CI, confidence interval; LHA, levator hiatus area; OR, odds ratio; PFMP, pelvic floor myofascial pain; SE, standard error; SWV, shear wave velocity; VM, Valsalva maneuver. Diagnostic nomogram of postpartum pelvic floor myofascial pain. ARA, anorectal angle; LHA, levator hiatus area; SWV, shear wave velocity; VM, Valsalva maneuver. Bootstrap resampling was performed 1,000 times for model validation. The ROC curve was plotted, with the area under the curve (AUC) values for the training and validation sets being 0.880 [95% confidence interval (CI): 0.836–0.924] and 0.861 (95% CI: 0.790–0.931), respectively. This indicated good performance of the nomogram in diagnosing postpartum PFMP ( Figure 5 ). ROC curves of nomogram model of training (A) and validation (B) sets. AUC, area under the curve; ROC, receiver operating characteristic. The HL test revealed no statistically significant difference between the actual and predicted probabilities of postpartum PFMP in the training set [χ 2 =13.285, degrees of freedom (df) =8, P=0.1024] and the validation set (χ 2 =14.567, df=8, P=0.06814). Calibration plots for the training and validation set models showed a high level of consistency between the model predictions and actual clinical observations ( Figure 6 ). Calibration curves of nomogram model of training (A) and validation (B) sets. As shown in Figure 7 , the model provided a net benefit across a wide threshold probability range of 11% to 100% in the training set. This clinical utility was confirmed in the validation set, with net benefits observed in the ranges of 3% to 87% and 92% to 100%. This indicated that using the model has good clinical utility, can aid in identifying postpartum PFMP, and can guide treatment decisions, thus providing clinical benefit. DCA curves of nomogram model of training (A) and validation (B) sets. DCA, decision curve analysis. To illustrate the utility of the nomogram, a description of its application in two example cases is provided. Case 1: a 32-year-old postpartum woman with an ARA excursion on maximum VM of 1°, a percentage change in LHA of 0.295, an SWVmean of 5.384 m/s and a weight gain during pregnancy of 13.2 kg, obtained a total score of 109 on the nomogram, corresponding to a diagnostic probability of 83%. The patient was diagnosed with postpartum PFMP based on the gold standard ( Figure 8 ). The first application example of nomogram model. (A) ARA measurement image, resting state ARA =115°, Valsalva state ARA =116°, ARA shift =1°. (B) Shear wave elastography measurement image, SWV mean =5.384 m/s. (C) Four-dimensional sonography, resting state LHA =18.3 cm 2 , Valsalva state LHA =23.69 cm 2 , percentage change in LHA =0.295. (D) According to the nomogram, SWVmean (12.5 points) + ARA excursion (76.5 points) + percentage change in LHA (5.5 points) + weight gain during pregnancy (14.5 points) = total score of 109 points, corresponding to a diagnostic probability of 83%. According to the gold standard diagnosis, it was classified as postpartum pelvic floor myofascial pain. ARA, anorectal angle; LHA, levator hiatus area; SWV, shear wave velocity; VM, Valsalva maneuver. Case 2: a 32-year-old postpartum woman with an ARA excursion on maximum VM of 12°, a percentage change in LHA of 0.556, an SWVmean of 3.238 m/s and weight gain during pregnancy of 11 kg, obtained a total score of 92 on the nomogram, corresponding to a diagnostic probability of 5%. However, she was not diagnosed with postpartum PFMP based on the gold standard ( Figure 9 ). The second application example of nomogram model. (A) ARA measurement image, resting state ARA =117°, Valsalva state ARA =129°, ARA excursion =12°. (B) Shear wave elastography measurement image, SWV mean =3.238 m/s. (C) Four-dimensional sonography, resting state LHA =14.2 cm 2 , Valsalva state LHA =22.1 cm 2 , percentage change in LHA =0.556. (D) According to the nomogram, SWVmean (4.5 points) + ARA excursion (71 points) + percentage change in LHA (4.5 points) + weight gain during pregnancy (12 points) = total score of 92 points, corresponding to a diagnostic probability of 5%. According to the gold standard diagnosis, it was classified as postpartum non-pelvic floor myofascial pain. ARA, anorectal angle; LHA, levator hiatus area; SWV, shear wave velocity; VM, Valsalva maneuver.

Postpartum PFMP is characterized by its high prevalence and complex clinical manifestations, however, the current assessment methods for this condition, which relying primarily on standardised palpation, are limited due to their invasive and subjective nature, often leading to diagnostic delays. The integrated model developed in our study, which combines sonographic parameters with clinical features, represents a non-invasive, quantitative, and clinically applicable assessment tool which has the potential to address the current deficiencies in objective diagnostic instruments. Previous studies have definitely established that prepregnancy obesity is a significant risk factor for postpartum pelvic floor dysfunction ( 31 ), which can include urinary and faecal incontinence. Our findings further indicate that excessive gestational weight gain also increases the risk of postpartum PFMP. We speculate that the underlying mechanism for this association may be similar to that by which prenatal obesity contributes to other pelvic floor disorders. Specifically, excessive weight gain during pregnancy elevates intra-abdominal pressure and leads to sustained compression of the PFM and fascial tissues via the enlarging uterus. This chronic mechanical load may induce prolonged tension in localized PFM, subsequently activating myofascial trigger points within the deep muscle layers and ultimately giving rise to the clinical symptoms of PFMP ( 32 ). ARA has high reliability and validity in the assessment of levator ani muscle (LAM) tone ( 33 , 34 ). The morphology and tension of LAM can be assessed via 4DUS imaging of the levator hiatus ( 35 , 36 ). A healthy pelvic floor musculature should demonstrate excellent compliance during increases in intra-abdominal pressure, which includes appropriate deformation and caudal displacement of the LAM, accompanied by enlargement of LHA and an increase in the ARA. In our study, dynamic pelvic floor ultrasound imaging was used to evaluate the biomechanical characteristics of the PFM. Notably, although no statistically significant differences were observed in the LHA and ARA between the PFMP and non-PFMP groups at rest or during the VM, we identified a crucial kinetic parameter: an inverse correlation was observed for both ARA excursion and LHA change percentage during VM, with smaller values correlating with a higher diagnostic probability of postpartum PFMP. This finding indicates that patients with PFMP exhibit decreased compliance of the LAM, which manifests as a reduced overall mobility and restricted deformation capacity. The results of this study may provide valuable guidance for clinical practice and support the transition in the assessment of postpartum PFMP from traditional static morphological observation to dynamic functional evaluation. SWE measures the speed of shear wave propagation through tissue, reflecting tissue stiffness ( 37 ). Morin et al. ( 38 ) reported that SWE measurements of PFM closely correlate with electromyography (EMG) findings and measurements of pelvic floor morphology, supporting its validity in assessing the stiffness of the PRM. Our study further found that the SWVmean of LAM serves as an independent diagnostic biomarker associated with postpartum PFMP. Comparative analysis revealed significantly higher SWVmean values in the PFMP group than in the non-PFMP group, suggesting altered in tissue biomechanical properties. The pathogenesis underlying the increased muscle stiffness in patients with PFMP is likely a product of multiple interrelated pathophysiological mechanisms ( 39 ): protective muscle spasms secondary to obstetric overstretching injuries during pregnancy and delivery can lead to pathological hypertonicity, localized chronic inflammatory responses can promote tissue edema and initiate profibrotic processes, and, concurrently, disrupted collagen metabolism within myofascial tissues may result in aberrant tissue remodeling. These microstructural alterations collectively drive the transition of PFMs from a physiologically compliant state to a pathologically rigid condition. Our study demonstrates that increased PFM stiffness as measured by SWE represents a biomechanical characteristic of PFMP. Consequently, SWVmean can serve as an objective and quantifiable parameter and holds promise as a sonographic biomarker for the diagnosis of PFMP. The primary contribution of this study is the development of a multivariate diagnostic model that can aid in the diagnosis of postpartum PFMP. Compared with a previously reported EMG-based prediction model for postpartum PFMP ( 4 ), the multimodal ultrasound nomogram developed in our study demonstrated notable improvements in diagnostic performance. When compared to traditional methods relying primarily on palpation assessment, transperineal ultrasound offers distinct advantages, including noninvasiveness, operational simplicity and excellent reproducibility, showing particular promise in evaluating PFM functional status. The existing EMG-based model reported in the literature showed room for improvement in terms of discrimination (AUC: 0.706) and clinical applicability (threshold probability: 23–72%). In contrast, our model achieved considerably higher AUC values of 0.880 and 0.861 in the training and validation sets, respectively. DCA further indicated substantially broader ranges of clinical applicability (training set: 11–100%; validation set: 3–87% and 92–100%). We attribute these improvements primarily to the ability of multimodal ultrasound to more directly assess the functional state of PFM from a biomechanical perspective. Unlike EMG parameters that mainly reflect electrophysiological activity or palpation assessments that depend on subjective judgment, ultrasonographic parameters allow for the objective quantitative assessment of PFM stiffness and function, potentially providing more comprehensive reference information for the diagnosis of PFMP. Additionally, the validation across two independent cohorts provides preliminary support for the robustness of our study. Several limitations to this study should be acknowledged. First, the diagnostic model was developed from a single-center cohort of Chinese postpartum women, which may reduce its generalizability. Specifically, the reliance on prepregnancy BMI as the clinical variable may lead to calibration bias if the model is applied to populations with different BMI distributions. Furthermore, the exclusion of obstetric factors (e.g., mode of delivery and birth weight) may have introduced unmeasured confounding and reduced the model’s discriminative accuracy in external populations. Therefore, external validation and potential recalibration are necessary before the model can be more broadly applied. Our model lacks data on perineal trauma, a factor known to be associated with postpartum PFMP. This omission may limit the nomogram’s comprehensiveness and diagnostic accuracy to an extent. Future work should integrate such obstetric injury measures to clarify the independent role of perineal trauma in PFMP. In our study, all assessments were conducted at 6–8 weeks postpartum, a period of ongoing muscular recovery. Longitudinal monitoring within the first postpartum year is needed to clarify the natural history of PFMP and inform long-term management. Standardised palpation remains the primary PFMP diagnostic method, yet it is subjective in nature. Our study developed an objective diagnostic model that incorporates quantifiable ultrasound and clinical parameters. Future research should explore the development of a multimodal diagnostic framework. In addition to incorporating ultrasonographic biomarkers, this framework should integrate patient-reported outcome measures ( 40 ) and other modes of clinical diagnostic criteria, such as the Visual Analogue Scale, Pelvic Floor Distress Inventory-20, Female Sexual Function Index, and EMG ( 41 ). The model developed in our study serves as a crucial component of this envisioned diagnostic framework. The multimodal ultrasound model demonstrated good performance but entails certain barriers related to cost and technical requirements. For clinical translation, we propose a stepwise approach consisting of validation in well-equipped centers followed by simplification of the technique through combination of key biomarkers with either low-cost ultrasound/artificial intelligence systems or basic clinical indicators. If completed, these steps may lead to the creation of accessible screening tools for primary care.

This study established a preliminary model for the diagnosis of postpartum PFMP by integrating clinical parameters with multimodal ultrasound indicators. The model demonstrated potential in terms of both diagnostic accuracy and clinical applicability, suggesting its possible utility as an adjunct diagnostic tool for the objective assessment of postpartum PFMP.

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