AI-Based Fundus Screening of Teachers: Fundus Tessellated Density as a Myopia Biomarker and Occupational Associations

AI-Based Fundus Screening of Teachers: Fundus Tessellated Density as a Myopia Biomarker and Occupational Associations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Based Fundus Screening of Teachers: Fundus Tessellated Density as a Myopia Biomarker and Occupational Associations Zhongnan Xiao, Huijuan Liu, Hongyan Wang, Min Song, Guoquan Liu, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8229056/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose This study was to investigate the practical application of artificial intelligence (AI)-based fundus screening system for ophthalmic health management in the teacher population, specifically aiming to explore the etiology and potential pathogenesis of high myopia-associated fundus lesion progression. Methods A randomized of primary and secondary school teachers from Beijing, provided color fundus images and health examination data. The study innovatively employed AI for assisted diagnosis and analysis. Results The mean spherical equivalent (SE) was − 2.43 ± 2.94 D. Univariate analysis identified significant correlations between SE and factors such as age, teaching subject, and near work duration. Quantitative analysis revealed that fundus tessellated density (FTD) was highest in the nasal macular quadrant. Both overall and nasal FTD within the 3 mm macular region effectively discriminated high myopia (SE ≤ -6.00 D), with area under the curve values of 0.7788 and 0.7631, respectively. Furthermore, teachers of literature subjects exhibited significantly lower SE and higher FTD, while prolonged near work (≥ 1 hour) at 30 cm was associated with increased FTD. Finally, FTD positively correlated with systemic immune-inflammatory indices (neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio), which were elevated in high myopia. Conclusion This study demonstrates the characteristics of the progression of myopia and fundus lesions in the teacher population through AI-based fundus screening system. AI-quantified FTD is a valuable biomarker for high myopia, is influenced by occupational near work, and is associated with a systemic pro-inflammatory state. artificial intelligence fundus image fundus tessellated density teachers deep learning near work myopia systemic immune-inflammation index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The human retina, a complex and light-sensitive tissue composed of diverse cell types, plays a critical role in vision. Retinal diseases are widely recognized as a leading cause of severe vision loss and blindness globally. However, disparities in the density and clinical experience of ophthalmologists across regions limit the accessibility of regular and accurate retinal disease screening [1 ; 2] . Advances in fundus imaging and artificial intelligence (AI) have enabled early, large-scale detection of fundus abnormalities in a resource-efficient manner [ 3 ] . In China, the Retinal Artificial Intelligence Diagnosis System (RAIDS) has demonstrated satisfactory performance in screening multiple retinal abnormalities using prospectively collected fundus images across tertiary hospitals, community hospitals, and physical examination centers, supporting its clinical applicability [4 ; 5] . This is particularly valuable in high-volume settings such as physical examination centers, where manual review of hundreds of fundus images daily is impractical. Deep learning-based systems offer rapid, safe, effective, and acceptable screening solutions [ 6 ] . Myopia, a leading cause of reversible visual impairment worldwide [ 7 ] , is associated with chorioretinal atrophy and sight-threatening complications such as retinal detachment, glaucoma, myopic macular degeneration, and choroidal neovascularization [8 ; 9] . Fundus tessellation, a common feature in myopic eyes, has been linked to retinal vein occlusion [ 10 ] and maculopathy [ 11 ] . Progression to macular atrophy may cause severe and irreversible vision loss [ 12 ] , contributing to public health burdens and productivity loss [ 13 ] . The rising global prevalence of myopia is influenced by environmental factors [ 14 ] , educational attainment, and occupational status [15 ; 16] . Occupational tasks involving prolonged near work, such as reading or using visual display terminals (VDTs) [ 17 ] , may exacerbate myopia progression. Nevertheless, studies on myopia and chorioretinal changes in specific occupational groups remain limited. Understanding fundus tessellation density (FTD) is essential for preventing retinochoroidal degeneration. In this study, we conducted a prospective, cluster-randomized investigation of myopic fundus changes among teachers using a deep learning-based AI system. 2. Results 2 .1. Baseline Characteristics A total of 648 teachers (96.29%) and 1,296 color fundus images were included in the analysis of the AI-based fundus screening system. Eyes with media opacities that prevented acquisition of gradable fundus photographs were excluded from image analysis. However, eyes with optic nerve or macular lesions were retained provided fundus image quality was assessable. The mean age of participants was 39.26 ± 8.47 years (median: 39 years; range: 22–74 years). The mean spherical equivalent (SE) was -2.43 ± 2.94 D (median: -1.00 D; range: -20.0 to 6.25 D). Among the participants, 61 (9.41%) were male and 587 (90.59%) were female. The mean SE was -2.43 ± 2.75 D (median: -1.38 D; range: -10.25 to 4.25 D) in males and -2.43 ± 2.96 D (median: -1.00 D; range: -20.00 to 6.25 D) in females. When stratifying participants by refractive status into normal (-0.25 D ≤ SE ≤ +0.25 D), hyperopic (SE > +0.25 D), and myopic (SE < -0.25 D) groups, myopic SE demonstrated the highest prevalence among all groups (Table 1). 2 .2. Analysis of Fundus Tessellated Density Using AI Technology in the Teacher Cohort Artificial intelligence-based image processing technology was employed to identify abnormal fundus features and quantify the average exposed choroidal area and atrophy arc area per unit area (Figure 1A, B). As shown in Table 2, univariate analysis revealed several significant correlations between spherical equivalent (SE) and systemic or ocular parameters. SE (categorized as hyperopic, normal, or myopic) was positively associated with older age, teaching subjects (main subjects, science subjects, literature subjects, art subjects, and others), alcohol consumption, higher frequency and intensity of exercise, greater medication usage, better naked eye vision, increased C/D ratio, and temporal neuroretinal rim width ( p < 0.05). In contrast, SE was negatively correlated with high education level, 30 cm and 50 cm-working distance duration, FTD, atrophic arc (AA), and astigmatism types (Regular astigmatism order: normal, rule astigmatism, against the rule astigmatism, oblique astigmatism) ( p < 0.05). No significant associations were observed with smoking, BMI, systolic and diastolic blood pressure, glycosylated hemoglobin, fasting blood glucose (FBG), total cholesterol, triglycerides, low-density lipoprotein and high-density lipoprotein, best corrected visual acuity (BCVA), intraocular pressure (IOP), and arteriovenous ratio (AVR). The mean FTD was 0.0609 ± 0.0743 (median, 0.0307; range, 0-0.4258). Univariate correlations between FTD and systemic or ocular factors are summarized in Table 3. FTD was positively associated with high education level, longer exercise duration, 30 cm-working distance duration, astigmatism types (regular astigmatism order: normal, rule astigmatism, against the rule astigmatism, oblique astigmatism) ( p < 0.05). Conversely, it was negatively associated with height, weight, teaching subjects, smoking, alcohol consumption, exercise intensity, triglycerides, astigmatism degree, better naked eye vision, C/D increased , BCVA, and temporal neuroretinal rim width ( p < 0.05). No significant correlations were detected for 50 cm-working distance duration, BMI, systolic and diastolic blood pressure, glycosylated hemoglobin, FBG, total cholesterol, low-density lipoprotein and high-density lipoprotein, IOP, and AVR. 2 .3. Quantitative Analysis of Fundus Tessellated Density in Macular Regions Among Myopic Teachers According to the Early Treatment Diabetic Retinopathy Study (ETDRS) grid, the macular area within a 6 mm diameter centered on the fovea was divided into two concentric circles with diameters of 3 mm (inner circle) and 6 mm (outer circle), respectively (Figure 2A). FTD was automatically quantified in four quadrants: superior (Sup), inferior (Inf), nasal (Nas), and temporal (Tem). Significant regional variations in FTD were observed within the 3 mm diameter region. The nasal quadrant exhibited significantly higher FTD compared to the Sup, Inf, and Tem quadrants. A similar pattern was observed in the 6 mm diameter region, where the Nas quadrant again demonstrated significantly elevated FTD relative to the other three quadrants. Additionally, within the 6 mm region, the Sup quadrant showed significantly higher FTD than the Tem quadrant (Figure 2B). 2 . 4 . ROC Analysis of FTD for Discriminating Myopia Severity Receiver operating characteristic (ROC) analysis demonstrated that AI-based assessment of FTD effectively differentiated between non-high myopia (SE > -6.00 D) and high myopia (SE ≤ -6.00 D). Both overall FTD and nasal FTD within the 3 mm diameter macular region showed discriminative capacity, though no statistically significant difference was observed between these two metrics (Figure 3A, B). The ROC curve for overall FTD yielded an area under the curve (AUC) of 0.7788 (95% CI: 0.7402-0.8175, p 0.06357, the sensitivity was 67.63% and specificity was 70.78%. For nasal FTD, the AUC was 0.7631 (95% CI: 0.7214-0.8048, p 0.0832 providing 71.10% sensitivity and 71.73% specificity. Consistent with these findings, both overall FTD and nasal FTD exhibited statistically significant differences between the high myopia and non-high myopia groups (Figure 3C, D). 2 . 5 . Quantitative Analysis of SE and FTD Across Academic Disciplines To evaluate the relationship between teaching subject specialization and ocular parameters, myopic teachers were categorized into five disciplinary groups: main subjects, science subjects, literature subjects, art subjects, and ‘others’. Analysis revealed that teachers specializing in literature subjects exhibited a significantly lower SE compared to the other four subject groups (Figure 4A). Consistent with this refractive pattern, literature subjects also demonstrated significantly higher FTD than both art subjects and the ‘others’ category. No statistically significant differences in FTD were observed among the main, science, and literature subject groups. Additionally, art subjects showed significantly reduced FTD compared to main subjects, while no significant difference was found between art subjects and the ‘others’ group (Figure 4B). 2 . 6 . Quantitative Analysis of FTD in Relation to Near Work Duration To evaluate the influence of near work duration on FTD in myopic teachers, participants were stratified into two groups based on daily near work exposure: < 1 hour and ≥ 1 hour. Initial analysis indicated that the left eye exhibited a significantly lower SE compared to the right eye (Figure 5A); therefore, subsequent FTD analyses included both eyes. When near work was categorized by working distance, the 30 cm-duration group showed a statistically significant reduction in SE in both eyes among participants with ≥ 1 hour of exposure (Figure 5B). In the 50 cm-duration group, a significant decrease in SE was observed only in the right eye, though values did not fall below those of the left eye (Figure 5C). Notably, no significant difference in FTD was observed between the < 1 hour and ≥ 1 hour groups in the 50 cm working distance condition (Figure 5D). Further analysis revealed that prolonged near work (≥ 1 hour) at 30 cm was associated with a significant increase in FTD within the 3 mm and 6 mm diameter macular regions centered on the fovea, though no such effect was observed in the 1 mm diameter region (Figure 5E). 2.7. Quantitative Analysis of FTD and Systemic Immune-Inflammatory Markers in High Myopia To further explore the etiology and potential pathogenesis of high myopia-related retinal lesions, we performed univariate analysis on systemic inflammatory markers. As summarized in Table 4, significant associations were observed between FTD and specific immunocyte subsets as well as systemic immune-inflammatory indices in the teacher cohort. Specifically, FTD showed a positive correlation with neutrophil percentage (NEU%) and systemic immune-inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), whereas a negative correlation was identified with lymphocyte percentage (LYM%). Comparative analysis further revealed that eyes with high myopia exhibited significantly elevated levels of NEU%, NLR, and PLR, along with a decreasing trend in lymphocyte percentage ( p = 0.0853), when compared to those with low or moderate myopia (Figure 6 A-D). These findings collectively underscore a close relationship between the progression of high myopia-related fundus lesions and a heightened systemic inflammatory state. 3. Materials and methods 3.1. Image collection All investigations were conducted in accordance with the Declaration of Helsinki and approved by the institutional review board of Beijing Rehabilitation Hospital Affiliated to Capital Medical University (No. 2021bkky-105). Each participant provided informed consent. To assess the use of AI in physical examination centers, a total of 1296 color fundus images from 648 individuals at a physical examination center were included for teacher population screening. Only one image per examined eye was included. Eyes that met any of the following criteria were excluded: poor image quality due to uncooperativeness or small pupils, and incomplete basic information. The inclusion process is outlined in Fig. 7 . 3.2 Ophthalmic and general examinations The examinations took place at the Model Worker Health Management Center, Beijing Rehabilitation Hospital, affiliated with Capital Medical University. Participants completed a standardized questionnaire covering demographic factors like age, gender, education, smoking, alcohol consumption, and teaching subjects (subject types: ① Main subjects: Chinese, Mathematics, English; ② Science subjects: Biology, Physics, Chemistry; ③ Literature subjects: Taoism, Politics, Geography, History; ④ Art subjects: music, art, sports; ⑤ ‘Others’: such as logistics, administration). They also reported exercise frequency (basically not, once a month, 3–5 times per week, 1–2 times per week, daily), intensity (mild, moderate, severe), duration (0.5 h, 0.5-1 h, > 1 h), near work habits (distance: 30 cm or 50 cm; duration: <1 h or ≥ 1 h), astigmatism types (regular astigmatism order: normal, rule astigmatism, against the rule astigmatism, oblique astigmatism), and more. Blood samples were collected for lipid, glycosylated hemoglobin, and glucose measurements after obtaining consent. Blood pressure, height, weight, and BMI were recorded. Ophthalmic assessments included vision acuity (nake vision, best corrected visual acuity (BCVA)), intraocular pressure (IOP) measurement (non-contact tonometer, TX-20, Canon, Japan), computer optometry (HUVITZ, HRK7000A), and fundus photography (nonstereoscopic photograph of 45° of the central fundus; fundus camera type CR-2 AF; Canon, Japan). Spherical equivalent (SE = spherical degrees + (cylindrical degrees/2)) was calculated for myopia (SE + 0.25 D), and normal vision (− 0.25 D ≤ SE ≤ + 0.25 D) classifications. 3.3 AI data collection The EVisionAI software, developed by EVision Technology (Beijing) Co. Ltd., is a color fundus image intelligent analysis tool that mimics the human visual system [ 37 ] . It utilizes advanced AI image processing techniques for extraction, denoising, normalization, and enhancement. The software accurately segments the optic disc and optic cup by combining a depth target detection network with a computer vision edge extraction algorithm [ 34 ] . Arterial and venous vessels are segmented using a ResNet101-UNet [ 38 ] deep learning model based on vessel features and topological relationships. Subsequently, the software provides detailed digital descriptions of color fundus photographs, including measurements such as optic disc and optic cup diameter, cup-to-disc ratio (C/D), vascular parameters, and fundus tessellated density (FTD). Previous studies have demonstrated segmentation and extraction accuracies of ≥ 0.96 for the optic disc, optic cup, peripapillary atrophy, and blood vessels, with sensitivity ≥ 0.85 and specificity ≥ 0.96 [ 39 ] . 3.4 Statistical analysis All data analysis was conducted using SPSS Statistics version 26.0 (IBM SPSS Statistics, USA) and GraphPad Prism 7.0 software. The normality of the distribution of all variables was assessed using the Shapiro-Wilk test and presented as the mean ± standard deviation (95% confidence intervals). The detection ratio of fundus abnormalities was compared using the chi-square test. Pearson linear regression analyses were employed to examine the relationship between SE or FTD (as an independent variable) and systemic and ocular parameters. Two-group comparisons were made with unpaired two-tailed Student’s t -test. For comparing the mean values of multiple groups, one-way ANOVA followed by Tukey’s multiple comparisons test was utilized to determine where those differences occurred. The criterion for statistical significance was set at p < 0.05. 4. Discussion The present study represents one of the first comprehensive investigations to integrate occupational heterogeneity with quantitative retinal analysis, focusing on the teaching profession—a group characterized by intensive near-work and VDTs use, particularly in the post-pandemic era [2 ; 18] . This prospective, cluster-randomized investigation leveraged a deep learning-based AI system to perform a quantitative analysis of FTD within a substantial cohort of teachers. Our study yields several principal findings: (1) FTD exhibits a distinct non-uniform topographic distribution across the macula, with a pronounced concentration in the nasal quadrant; (2) AI-quantified FTD serves as a robust, objective biomarker for discriminating high myopia, demonstrating significant diagnostic efficacy; (3) Specific occupational and behavioral factors, including academic discipline and sustained near work at a critical distance of 30 cm, are significantly correlated with greater myopic severity and elevated FTD; and (4) A novel association exists between systemic immune-inflammatory indices and high myopia, suggesting a potential role for inflammation in its pathogenesis. Collectively, these findings underscore the transformative potential of AI in delivering precise retinal phenotyping and illuminate the complex, multifactorial interplay underlying myopic chorioretinal degeneration. The application of AI in diagnostic imaging has been validated across multiple medical disciplines, including radiology and dermatology, and is increasingly recognized for its utility in ophthalmology [ 19 ] . FTD represents the earliest stage of myopic maculopathy, and its progression may lead to macular atrophy and irreversible vision loss [ 20 ] . In this study, we demonstrated that FTD, a recognized quantitative biomarker of choroidal thickness [21 ; 22] , can be effectively measured using a deep learning system and is strongly associated with occupational and behavioral factors. The observed regional heterogeneity of FTD within the macula, with its peak in the nasal quadrant, quantitatively validates and extends previous observations from histological and qualitative clinical studie s[ 23 ] . This pattern is likely a biomechanical consequence of axial elongation, wherein the nasal choroid may be structurally more vulnerable to stretching and thinning compared to other regions [ 24 ] . The consistent finding of the lowest FTD in the temporal quadrant further reinforces the concept of regional susceptibility, potentially afforded by the structural buttressing effect of the optic nerve head [ 25 ] . Our AI system’s ability to precisely map this gradient underscores its superiority over subjective grading, enabling a more sensitive assessment of chorioretinal health. The AUC values exceeding 0.76 for both overall and nasal FTD confirm a good diagnostic performance, comparable to or surpassing other proposed imaging biomarkers [26 ; 27] . This positions FTD assessment as a viable tool for integration into high-throughput screening environments, such as the physical examination centers referenced in our introduction, to efficiently identify individuals warranting specialist care for high myopia management. The comparable performance of overall and nasal FTD suggests that a global assessment may be pragmatically sufficient for initial screening, though quadrant-specific analysis might offer unique insights for monitoring progression in research or detailed clinical settings [9 ; 11] . The relationship between near work and myopia progression remains a subject of ongoing debate [ 28 ] . While sustained accommodative effort [29 ; 30] , prolonged reading, and shorter working distances [31 ; 32] have been implicated in myopia pathogenesis, empirical evidence in occupational cohorts remains limited. Delving into occupational determinants, we found that teachers specializing in literature exhibited the most pronounced myopic refractive status and the highest FTD. This association is plausibly mediated by the exceptionally high volume of sustained reading and near-work intrinsic to this discipline, which may potentiate axial elongation and subsequent chorioretinal atrophy [ 33 ] . This hypothesis is strongly corroborated by our dose-response analysis of near-work, which revealed that prolonged exposure (≥ 1 hour once) at a short working distance of 30 cm was a significant risk factor for both a more myopic SE and increased FTD in the central macula. This provides compelling, in-vivo evidence linking a modifiable behavioral pattern directly to quantifiable structural damage in the retina, highlighting a critical intervention point for this at-risk profession. Several limitations merit consideration. First, the overwhelming female predominance in our cohort, while reflective of the local teaching demographic, may affect the generalizability of our findings to male populations. Second, the cross-sectional nature of the analysis establishes association but not causation. Prospective, longitudinal studies are essential to determine if FTD progression predicts the future incidence of myopic complications. Third, the performance of our AI model, though validated internally, requires external validation in diverse, multi-ethnic populations to confirm its broad applicability [ 34 ] . Finally, axial length (AL), a critical confounding factor strongly correlated with choroidal thickness [35 ; 36] , was not measured due to time constraints during large-scale screening. Future studies should incorporate AL measurement and longitudinal follow-up to validate and extend these findings. In conclusion, this study harnesses the power of AI technology to provide an unprecedented, quantitative characterization of fundus tessellation in a defined occupational cohort. We validate FTD as a sensitive imaging biomarker for high myopia and delineate its significant associations with occupational specialization, critical near-work behavior, and systemic inflammation. These insights underscore the necessity for targeted occupational health strategies and lifestyle modifications for at-risk groups. The integration of AI-based FTD quantification into large-scale screening paradigms holds immense promise for the early identification, risk stratification, and timely management of individuals along the sight-threatening pathway of myopic chorioretinal degeneration. 5. Conclusion In summary, this prospective cluster-randomized study demonstrates the significant utility of deep learning-based AI systems in quantifying fundus tessellation density and elucidating its association with occupational factors in a teacher cohort. Our findings reveal that occupational specificity, particularly teaching literature subjects, and behavioral factors including prolonged near work at shorter distances (30 cm) are independently associated with myopia progression and increased FTD. Regional analysis further identifies the nasal macular quadrant as particularly vulnerable to tessellation changes during axial elongation. The strong discriminative performance of both overall FTD (AUC = 0.7788) and nasal FTD (AUC = 0.7631) in detecting high myopia supports their potential as reliable screening biomarkers. Additionally, the elevated systemic immune-inflammatory markers (NLR and PLR) in high myopia groups suggest a potential role of systemic inflammation in myopia pathogenesis. These findings highlight the value of AI-assisted fundus imaging in occupational health screening and underscore the importance of developing targeted intervention strategies for high-risk professional groups. Future longitudinal studies incorporating axial length measurement and mechanistic investigations into inflammatory pathways will further enhance our understanding of occupation-related myopic progression. Declarations Acknowledgments We would like to express our gratitude to Dr. Yu Hong and the Model Worker Health Management Center of Beijing Rehabilitation Hospital for their valuable data support. We thank EVision Technology (Beijing) Co. Ltd. for their EVisionAI software for data analysis. Funding This work was supported by Science and Technology Development Special Fund, Beijing Rehabilitation Hospital, Capital Medical University (grant numbers 2021-071). Conflict of interest The authors declare they have no competing interests. Ethics approval and consent to participate All investigations were conducted in accordance with the Declaration of Helsinki and approved by the institutional review board of Beijing Rehabilitation Hospital Affiliated to Capital Medical University (No. 2021bkky-105). Each participant provided informed consent. Availability of data All data supporting the findings of this study are available within the paper and its Supplementary Information. Author contributions Conceptualization: Z. X., H. L., and H. Y.. Investigation: Z. X., H. L., H. W., M. S., G. L., C. Y., L. L., M. L., J. S., Y. R., and Y. G.. Data Curation and Formal Analysis: Z. X. and H. L.. Project Administration: Z. X., H. L., Y. Z., H. Y., and Y. L.. Writing – original draft: Z. X.. 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Front Cell Dev Biol, 2023, 11: 1174984. DOI: 10.3389/fcell.2023.1174984. Liu F, Yu X H, Wang Y C, Cao M, Xie L F, Liu J, Liu L L. Quantitative analysis of optic disc changes in school-age children with ametropia based on artificial intelligence. Int J Ophthalmol, 2023, 16（11）: 1727-1733. DOI: 10.18240/ijo.2023.11.01. Tables Tables 1 to 4 are available in the supplementary files section Additional Declarations No competing interests reported. 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13:01:13","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":134726,"visible":true,"origin":"","legend":"","description":"","filename":"53c52021e3314a1c955b75645bfb72f01structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/82287effaccd0d52b2e1a14b.xml"},{"id":100388423,"identity":"8489edcb-0cae-4b62-813e-7a7ee774081a","added_by":"auto","created_at":"2026-01-16 11:17:28","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":150470,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/d2d9dc8b971faf34eec67fc4.html"},{"id":100388378,"identity":"48ef493d-34ad-4623-87c9-7e17d6b99355","added_by":"auto","created_at":"2026-01-16 11:17:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":107770,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFundus structure identification and detection rate of FTD in the teacher cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Enhanced color fundus photograph. (B) AI-derived distribution heatmap of FTD. Regions with high FTD are indicated in red, low FTD in blue, and atrophic plaques are represented as deep red areas.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/c514d7979199f1ccd28b19f7.png"},{"id":100388494,"identity":"0fe93e45-a386-4041-89af-7ae4204cb0c9","added_by":"auto","created_at":"2026-01-16 11:17:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":101035,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional distribution of FTD in the macular area of myopic teachers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u0026nbsp;\u0026nbsp;\u003c/strong\u003eDivision of the macular region according to the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. A 6 mm diameter area centered on the fovea was partitioned into two concentric circles: an inner circle (3 mm diameter) and an outer circle (6 mm diameter). Each circle was subdivided into four quadrants—superior (Sup), inferior (Inf), nasal (Nas), and temporal (Tem)—by an X-shaped yellow line. (B)\u0026nbsp;Quantitative analysis of FTD across the four quadrants. The result showed mean ± SD, n = 923 eyes. One-way ANOVA followed by Tukey's multiple comparisons test. Nas \u003cem\u003evs.\u003c/em\u003e Sup, Inf, and Tem, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001; Tem \u003cem\u003evs.\u003c/em\u003e Sup, \u003csup\u003e#\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/1fccf7f0b19075be57c53f9a.png"},{"id":100387860,"identity":"edaab8cf-dfad-4446-b3a2-11d69fbe0fb5","added_by":"auto","created_at":"2026-01-16 11:16:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":108207,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiagnostic performance of FTD in predicting high myopia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A, B) ROC curves for overall FTD and nasal FTD in discriminating high myopia (SE ≤ -6.00 D). (C, D) Comparisons of overall FTD and nasal FTD between the high myopia (SE ≤ -6.00 D) and non-high myopia (SE \u0026gt; -6.00 D) groups.\u0026nbsp;The Student’s \u003cem\u003et\u003c/em\u003e-test was followed by Welch’s correction. n = 173-421 eyes. ≤-6.00 D \u003cem\u003evs.\u003c/em\u003e \u0026gt; -6.00 D, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/79eca9782a7227461c20cfa9.png"},{"id":100388346,"identity":"271b7090-7058-4608-9fad-54365455a5b0","added_by":"auto","created_at":"2026-01-16 11:17:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":53960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSE and FTD distribution across teaching disciplines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) SE values for different subjects were showed. (B) FTD values for different subjects were displayed. (A, B) The results showed mean ± SD, n = 82-385 eyes. One way ANOVA followed by Tukey's multiple comparisons test. Literature subjects \u003cem\u003evs.\u003c/em\u003e the other four subjects, \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001 (A). Literature subjects \u003cem\u003evs. \u003c/em\u003eart subjects and ‘others’,\u003csup\u003e *\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01; art subjects \u003cem\u003evs.\u003c/em\u003e main subjects, \u003csup\u003e#\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/07f349b41974773566db89fa.png"},{"id":100388466,"identity":"60dc7f92-7667-4a71-9ce9-5af094815315","added_by":"auto","created_at":"2026-01-16 11:17:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between near work duration and FTD in myopic teachers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Comparison of FTD between 30 cm and 50 cm near-work distance groups (n = 185–758 eyes). (B–D) Regional analysis of FTD within 1 mm, 3 mm, and 6 mm diameter macular regions centered on the fovea, stratified by near-work duration (\u0026lt; 1 hour \u003cem\u003evs. \u003c/em\u003e≥ 1 hour). n=260-683 eyes. (A-D) Student’s \u003cem\u003et\u003c/em\u003e-test followed by Welch’s correction. ≥ 1 h \u003cem\u003evs. \u003c/em\u003e\u0026lt; 1 h, ns., \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/6f8730209d069fe7799a1e66.png"},{"id":100388419,"identity":"5fb4c9b9-8d51-4aa8-8652-a52eb45e3ca0","added_by":"auto","created_at":"2026-01-16 11:17:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of FTD with systemic immune-inflammatory indices in high myopia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-D) Comparisons of NEU%, NLR, PLR, and LYM% between high myopia (SE ≤ –6.00 D) and non-high myopia (SE \u0026gt; –6.00 D) groups. The Student’s \u003cem\u003et\u003c/em\u003e-test was followed by Welch’s correction. n = 265 eyes. ≤ -6.00 D \u003cem\u003evs.\u003c/em\u003e \u0026gt; -6.00 D, \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/c0a05e75ed8b00478681dda2.png"},{"id":100388504,"identity":"1fc9f757-0fcb-4891-adb8-d587954580fc","added_by":"auto","created_at":"2026-01-16 11:17:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":76860,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart for testing the AI fundus screening system ophthalmic and general examinations\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/ad48550ff3f8cbe281bf85c7.png"},{"id":100593897,"identity":"bc17cbc5-5b37-49ed-876a-da503697bfa3","added_by":"auto","created_at":"2026-01-19 13:25:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1515493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/cb67a2fb-7171-4345-a890-b5f1fc3d964e.pdf"},{"id":100388103,"identity":"d2ba9063-9039-45ef-8f06-628ad8bac0b7","added_by":"auto","created_at":"2026-01-16 11:17:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":27740523,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8229056/v1/3e5151cebae1c942b74327bf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AI-Based Fundus Screening of Teachers: Fundus Tessellated Density as a Myopia Biomarker and Occupational Associations","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human retina, a complex and light-sensitive tissue composed of diverse cell types, plays a critical role in vision. Retinal diseases are widely recognized as a leading cause of severe vision loss and blindness globally. However, disparities in the density and clinical experience of ophthalmologists across regions limit the accessibility of regular and accurate retinal disease screening\u003csup\u003e[1\u003c/sup\u003e; \u003csup\u003e2]\u003c/sup\u003e. Advances in fundus imaging and artificial intelligence (AI) have enabled early, large-scale detection of fundus abnormalities in a resource-efficient manner\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In China, the Retinal Artificial Intelligence Diagnosis System (RAIDS) has demonstrated satisfactory performance in screening multiple retinal abnormalities using prospectively collected fundus images across tertiary hospitals, community hospitals, and physical examination centers, supporting its clinical applicability\u003csup\u003e[4\u003c/sup\u003e; \u003csup\u003e5]\u003c/sup\u003e. This is particularly valuable in high-volume settings such as physical examination centers, where manual review of hundreds of fundus images daily is impractical. Deep learning-based systems offer rapid, safe, effective, and acceptable screening solutions\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMyopia, a leading cause of reversible visual impairment worldwide\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, is associated with chorioretinal atrophy and sight-threatening complications such as retinal detachment, glaucoma, myopic macular degeneration, and choroidal neovascularization\u003csup\u003e[8\u003c/sup\u003e; \u003csup\u003e9]\u003c/sup\u003e. Fundus tessellation, a common feature in myopic eyes, has been linked to retinal vein occlusion\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e and maculopathy\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Progression to macular atrophy may cause severe and irreversible vision loss\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, contributing to public health burdens and productivity loss\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The rising global prevalence of myopia is influenced by environmental factors\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, educational attainment, and occupational status\u003csup\u003e[15\u003c/sup\u003e; \u003csup\u003e16]\u003c/sup\u003e. Occupational tasks involving prolonged near work, such as reading or using visual display terminals (VDTs)\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, may exacerbate myopia progression. Nevertheless, studies on myopia and chorioretinal changes in specific occupational groups remain limited. Understanding fundus tessellation density (FTD) is essential for preventing retinochoroidal degeneration. In this study, we conducted a prospective, cluster-randomized investigation of myopic fundus changes among teachers using a deep learning-based AI system.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.1.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBaseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 648 teachers (96.29%) and 1,296 color fundus images were included in the analysis of the AI-based fundus screening system. Eyes with media opacities that prevented acquisition of gradable fundus photographs were excluded from image analysis. However, eyes with optic nerve or macular lesions were retained provided fundus image quality was assessable.\u003c/p\u003e\n\u003cp\u003eThe mean age of participants was 39.26 \u0026plusmn; 8.47 years (median: 39 years; range: 22\u0026ndash;74 years). The mean spherical equivalent (SE) was -2.43 \u0026plusmn; 2.94 D (median: -1.00 D; range: -20.0 to 6.25 D). Among the participants, 61 (9.41%) were male and 587 (90.59%) were female. The mean SE was -2.43 \u0026plusmn; 2.75 D (median: -1.38 D; range: -10.25 to 4.25 D) in males and -2.43 \u0026plusmn; 2.96 D (median: -1.00 D; range: -20.00 to 6.25 D) in females. When stratifying participants by refractive status into normal (-0.25 D \u0026le; SE \u0026le; +0.25 D), hyperopic (SE \u0026gt; +0.25 D), and myopic (SE \u0026lt; -0.25 D) groups, myopic SE demonstrated the highest prevalence among all groups (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis of Fundus Tessellated Density Using AI Technology in the Teacher Cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArtificial intelligence-based image processing technology was employed to identify abnormal fundus features and quantify the average exposed choroidal area and atrophy arc area per unit area\u0026nbsp;(Figure 1A, B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, univariate analysis revealed several significant correlations between spherical equivalent (SE) and systemic or ocular parameters. SE (categorized as hyperopic, normal, or myopic) was positively associated with\u0026nbsp;older age, teaching subjects (main subjects, science subjects, literature subjects, art subjects, and others), alcohol consumption, higher frequency and intensity of exercise, greater medication usage, better naked eye vision, increased C/D ratio, and temporal neuroretinal rim width (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05).\u0026nbsp;In contrast, SE was negatively correlated with\u0026nbsp;high education level, 30 cm and 50 cm-working distance duration, FTD, atrophic arc (AA), and astigmatism types (Regular astigmatism order: normal, rule astigmatism, against the rule astigmatism, oblique astigmatism) (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05).\u0026nbsp;No significant associations were observed with\u0026nbsp;smoking, BMI, systolic and diastolic blood pressure, glycosylated hemoglobin, fasting blood glucose (FBG), total cholesterol, triglycerides, low-density lipoprotein and high-density lipoprotein, best corrected visual acuity (BCVA), intraocular pressure (IOP), and arteriovenous ratio (AVR).\u003c/p\u003e\n\u003cp\u003eThe mean FTD was 0.0609 \u0026plusmn; 0.0743 (median, 0.0307; range, 0-0.4258).\u0026nbsp;Univariate correlations between FTD and systemic or ocular factors are summarized in Table 3.\u0026nbsp;FTD was positively associated with high education level, longer exercise duration, 30 cm-working distance duration, astigmatism types (regular astigmatism order: normal, rule astigmatism, against the rule astigmatism, oblique astigmatism) (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). Conversely, it was negatively associated with height, weight, teaching subjects, smoking, alcohol consumption, exercise intensity, triglycerides, astigmatism degree, better naked eye vision, C/D increased , BCVA, and temporal neuroretinal rim width (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05).\u0026nbsp;No significant correlations were detected for\u0026nbsp;50 cm-working distance duration, BMI, systolic and diastolic blood pressure, glycosylated hemoglobin, FBG, total cholesterol, low-density lipoprotein and high-density lipoprotein, IOP, and AVR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.3.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eQuantitative Analysis of Fundus Tessellated Density in Macular Regions Among Myopic Teachers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the Early Treatment Diabetic Retinopathy Study (ETDRS) grid, the macular area within a 6 mm diameter centered on the fovea was divided into two concentric circles with diameters of 3 mm (inner circle) and 6 mm (outer circle), respectively (Figure 2A). FTD was automatically quantified in four quadrants: superior (Sup), inferior (Inf), nasal (Nas), and temporal (Tem).\u003c/p\u003e\n\u003cp\u003eSignificant regional variations in FTD were observed within the 3 mm diameter region. The nasal quadrant exhibited significantly higher FTD compared to the Sup, Inf, and Tem quadrants. A similar pattern was observed in the 6 mm diameter region, where the Nas quadrant again demonstrated significantly elevated FTD relative to the other three quadrants. Additionally, within the 6 mm region, the Sup quadrant showed significantly higher FTD than the Tem quadrant (Figure 2B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eROC Analysis of FTD for Discriminating Myopia Severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) analysis demonstrated that AI-based assessment of FTD effectively differentiated between non-high myopia (SE \u0026gt; -6.00 D) and high myopia (SE \u0026le; -6.00 D). Both overall FTD and nasal FTD within the 3 mm diameter macular region showed discriminative capacity, though no statistically significant difference was observed between these two metrics\u0026nbsp;(Figure 3A, B). The ROC curve for overall FTD yielded an area under the curve (AUC) of 0.7788 (95% CI: 0.7402-0.8175, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001). At an optimal cutoff value of \u0026gt; 0.06357, the sensitivity was 67.63% and specificity was 70.78%. For nasal FTD, the AUC was 0.7631 (95% CI: 0.7214-0.8048, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001), with a cutoff of \u0026gt; 0.0832 providing 71.10% sensitivity and 71.73% specificity. Consistent with these findings, both overall FTD and nasal FTD exhibited statistically significant differences between the high myopia and non-high myopia groups (Figure 3C, D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eQuantitative Analysis of SE and FTD Across Academic Disciplines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the relationship between teaching subject specialization and ocular parameters, myopic teachers were categorized into five disciplinary groups: main subjects, science subjects, literature subjects, art subjects, and \u0026lsquo;others\u0026rsquo;. Analysis revealed that teachers specializing in literature subjects exhibited a significantly lower SE compared to the other four subject groups (Figure 4A). Consistent with this refractive pattern, literature subjects also demonstrated significantly higher FTD than both art subjects and the \u0026lsquo;others\u0026rsquo; category. No statistically significant differences in FTD were observed among the main, science, and literature subject groups. Additionally, art subjects showed significantly reduced FTD compared to main subjects, while no significant difference was found between art subjects and the \u0026lsquo;others\u0026rsquo; group (Figure 4B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eQuantitative Analysis of FTD in Relation to Near Work Duration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the influence of near work duration on FTD in myopic teachers, participants were stratified into two groups based on daily near work exposure: \u0026lt; 1 hour and \u0026ge; 1 hour. Initial analysis indicated that the left eye exhibited a significantly lower SE compared to the right eye (Figure 5A); therefore, subsequent FTD analyses included both eyes.\u003c/p\u003e\n\u003cp\u003eWhen near work was categorized by working distance, the 30 cm-duration group showed a statistically significant reduction in SE in both eyes among participants with \u0026ge; 1 hour of exposure (Figure 5B). In the 50 cm-duration group, a significant decrease in SE was observed only in the right eye, though values did not fall below those of the left eye (Figure 5C). Notably, no significant difference in FTD was observed between the \u0026lt; 1 hour and \u0026ge; 1 hour groups in the 50 cm working distance condition (Figure 5D). Further analysis revealed that prolonged near work (\u0026ge; 1 hour) at 30 cm was associated with a significant increase in FTD within the 3 mm and 6 mm diameter macular regions centered on the fovea, though no such effect was observed in the 1 mm diameter region (Figure 5E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7. Quantitative Analysis of FTD and Systemic Immune-Inflammatory Markers in High Myopia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore the etiology and potential pathogenesis of high myopia-related retinal lesions, we performed univariate analysis on systemic inflammatory markers.\u0026nbsp;As summarized in\u0026nbsp;Table 4, significant associations were observed between FTD and specific immunocyte subsets as well as systemic immune-inflammatory indices in the teacher cohort. Specifically, FTD showed a positive correlation with neutrophil percentage (NEU%) and systemic immune-inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), whereas a negative correlation was identified with lymphocyte percentage (LYM%). \u0026nbsp;Comparative analysis further revealed that eyes with high myopia exhibited significantly elevated levels of NEU%, NLR, and PLR, along with a decreasing trend in lymphocyte percentage (\u003cem\u003ep\u003c/em\u003e = 0.0853), when compared to those with low or moderate myopia (Figure 6 A-D). These findings collectively underscore a close relationship between the progression of high myopia-related fundus lesions and a heightened systemic inflammatory state.\u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Image collection\u003c/h2\u003e \u003cp\u003e All investigations were conducted in accordance with the Declaration of Helsinki and approved by the institutional review board of Beijing Rehabilitation Hospital Affiliated to Capital Medical University (No. 2021bkky-105). Each participant provided informed consent. To assess the use of AI in physical examination centers, a total of 1296 color fundus images from 648 individuals at a physical examination center were included for teacher population screening. Only one image per examined eye was included. Eyes that met any of the following criteria were excluded: poor image quality due to uncooperativeness or small pupils, and incomplete basic information. The inclusion process is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Ophthalmic and general examinations\u003c/h2\u003e \u003cp\u003eThe examinations took place at the Model Worker Health Management Center, Beijing Rehabilitation Hospital, affiliated with Capital Medical University. Participants completed a standardized questionnaire covering demographic factors like age, gender, education, smoking, alcohol consumption, and teaching subjects (subject types: ① Main subjects: Chinese, Mathematics, English; ② Science subjects: Biology, Physics, Chemistry; ③ Literature subjects: Taoism, Politics, Geography, History; ④ Art subjects: music, art, sports; ⑤ \u0026lsquo;Others\u0026rsquo;: such as logistics, administration). They also reported exercise frequency (basically not, once a month, 3\u0026ndash;5 times per week, 1\u0026ndash;2 times per week, daily), intensity (mild, moderate, severe), duration (0.5 h, 0.5-1 h, \u0026gt;\u0026thinsp;1 h), near work habits (distance: 30 cm or 50 cm; duration: \u0026lt;1 h or \u0026ge;\u0026thinsp;1 h), astigmatism types (regular astigmatism order: normal, rule astigmatism, against the rule astigmatism, oblique astigmatism), and more. Blood samples were collected for lipid, glycosylated hemoglobin, and glucose measurements after obtaining consent. Blood pressure, height, weight, and BMI were recorded. Ophthalmic assessments included vision acuity (nake vision, best corrected visual acuity (BCVA)), intraocular pressure (IOP) measurement (non-contact tonometer, TX-20, Canon, Japan), computer optometry (HUVITZ, HRK7000A), and fundus photography (nonstereoscopic photograph of 45\u0026deg; of the central fundus; fundus camera type CR-2 AF; Canon, Japan). Spherical equivalent (SE\u0026thinsp;=\u0026thinsp;spherical degrees + (cylindrical degrees/2)) was calculated for myopia (SE\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.25 D), hypermetropia (SE\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;0.25 D), and normal vision (\u0026minus;\u0026thinsp;0.25 D\u0026thinsp;\u0026le;\u0026thinsp;SE\u0026thinsp;\u0026le;\u0026thinsp;+\u0026thinsp;0.25 D) classifications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 AI data collection\u003c/h2\u003e \u003cp\u003eThe EVisionAI software, developed by EVision Technology (Beijing) Co. Ltd., is a color fundus image intelligent analysis tool that mimics the human visual system\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. It utilizes advanced AI image processing techniques for extraction, denoising, normalization, and enhancement. The software accurately segments the optic disc and optic cup by combining a depth target detection network with a computer vision edge extraction algorithm\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Arterial and venous vessels are segmented using a ResNet101-UNet\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e deep learning model based on vessel features and topological relationships. Subsequently, the software provides detailed digital descriptions of color fundus photographs, including measurements such as optic disc and optic cup diameter, cup-to-disc ratio (C/D), vascular parameters, and fundus tessellated density (FTD). Previous studies have demonstrated segmentation and extraction accuracies of \u0026ge;\u0026thinsp;0.96 for the optic disc, optic cup, peripapillary atrophy, and blood vessels, with sensitivity\u0026thinsp;\u0026ge;\u0026thinsp;0.85 and specificity\u0026thinsp;\u0026ge;\u0026thinsp;0.96\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll data analysis was conducted using SPSS Statistics version 26.0 (IBM SPSS Statistics, USA) and GraphPad Prism 7.0 software. The normality of the distribution of all variables was assessed using the Shapiro-Wilk test and presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (95% confidence intervals). The detection ratio of fundus abnormalities was compared using the chi-square test. Pearson linear regression analyses were employed to examine the relationship between SE or FTD (as an independent variable) and systemic and ocular parameters. Two-group comparisons were made with unpaired two-tailed Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test. For comparing the mean values of multiple groups, one-way ANOVA followed by Tukey\u0026rsquo;s multiple comparisons test was utilized to determine where those differences occurred. The criterion for statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study represents one of the first comprehensive investigations to integrate occupational heterogeneity with quantitative retinal analysis, focusing on the teaching profession\u0026mdash;a group characterized by intensive near-work and VDTs use, particularly in the post-pandemic era\u003csup\u003e[2\u003c/sup\u003e; \u003csup\u003e18]\u003c/sup\u003e. This prospective, cluster-randomized investigation leveraged a deep learning-based AI system to perform a quantitative analysis of FTD within a substantial cohort of teachers. Our study yields several principal findings: (1) FTD exhibits a distinct non-uniform topographic distribution across the macula, with a pronounced concentration in the nasal quadrant; (2) AI-quantified FTD serves as a robust, objective biomarker for discriminating high myopia, demonstrating significant diagnostic efficacy; (3) Specific occupational and behavioral factors, including academic discipline and sustained near work at a critical distance of 30 cm, are significantly correlated with greater myopic severity and elevated FTD; and (4) A novel association exists between systemic immune-inflammatory indices and high myopia, suggesting a potential role for inflammation in its pathogenesis. Collectively, these findings underscore the transformative potential of AI in delivering precise retinal phenotyping and illuminate the complex, multifactorial interplay underlying myopic chorioretinal degeneration.\u003c/p\u003e \u003cp\u003eThe application of AI in diagnostic imaging has been validated across multiple medical disciplines, including radiology and dermatology, and is increasingly recognized for its utility in ophthalmology\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. FTD represents the earliest stage of myopic maculopathy, and its progression may lead to macular atrophy and irreversible vision loss\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In this study, we demonstrated that FTD, a recognized quantitative biomarker of choroidal thickness\u003csup\u003e[21\u003c/sup\u003e; \u003csup\u003e22]\u003c/sup\u003e, can be effectively measured using a deep learning system and is strongly associated with occupational and behavioral factors. The observed regional heterogeneity of FTD within the macula, with its peak in the nasal quadrant, quantitatively validates and extends previous observations from histological and qualitative clinical studie\u003csup\u003es[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. This pattern is likely a biomechanical consequence of axial elongation, wherein the nasal choroid may be structurally more vulnerable to stretching and thinning compared to other regions\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The consistent finding of the lowest FTD in the temporal quadrant further reinforces the concept of regional susceptibility, potentially afforded by the structural buttressing effect of the optic nerve head\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur AI system\u0026rsquo;s ability to precisely map this gradient underscores its superiority over subjective grading, enabling a more sensitive assessment of chorioretinal health. The AUC values exceeding 0.76 for both overall and nasal FTD confirm a good diagnostic performance, comparable to or surpassing other proposed imaging biomarkers\u003csup\u003e[26\u003c/sup\u003e; \u003csup\u003e27]\u003c/sup\u003e. This positions FTD assessment as a viable tool for integration into high-throughput screening environments, such as the physical examination centers referenced in our introduction, to efficiently identify individuals warranting specialist care for high myopia management. The comparable performance of overall and nasal FTD suggests that a global assessment may be pragmatically sufficient for initial screening, though quadrant-specific analysis might offer unique insights for monitoring progression in research or detailed clinical settings\u003csup\u003e[9\u003c/sup\u003e; \u003csup\u003e11]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe relationship between near work and myopia progression remains a subject of ongoing debate\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. While sustained accommodative effort\u003csup\u003e[29\u003c/sup\u003e; \u003csup\u003e30]\u003c/sup\u003e, prolonged reading, and shorter working distances\u003csup\u003e[31\u003c/sup\u003e; \u003csup\u003e32]\u003c/sup\u003ehave been implicated in myopia pathogenesis, empirical evidence in occupational cohorts remains limited. Delving into occupational determinants, we found that teachers specializing in literature exhibited the most pronounced myopic refractive status and the highest FTD. This association is plausibly mediated by the exceptionally high volume of sustained reading and near-work intrinsic to this discipline, which may potentiate axial elongation and subsequent chorioretinal atrophy\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This hypothesis is strongly corroborated by our dose-response analysis of near-work, which revealed that prolonged exposure (\u0026ge;\u0026thinsp;1 hour once) at a short working distance of 30 cm was a significant risk factor for both a more myopic SE and increased FTD in the central macula. This provides compelling, in-vivo evidence linking a modifiable behavioral pattern directly to quantifiable structural damage in the retina, highlighting a critical intervention point for this at-risk profession.\u003c/p\u003e \u003cp\u003eSeveral limitations merit consideration. First, the overwhelming female predominance in our cohort, while reflective of the local teaching demographic, may affect the generalizability of our findings to male populations. Second, the cross-sectional nature of the analysis establishes association but not causation. Prospective, longitudinal studies are essential to determine if FTD progression predicts the future incidence of myopic complications. Third, the performance of our AI model, though validated internally, requires external validation in diverse, multi-ethnic populations to confirm its broad applicability\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Finally, axial length (AL), a critical confounding factor strongly correlated with choroidal thickness\u003csup\u003e[35\u003c/sup\u003e; \u003csup\u003e36]\u003c/sup\u003e, was not measured due to time constraints during large-scale screening. Future studies should incorporate AL measurement and longitudinal follow-up to validate and extend these findings.\u003c/p\u003e \u003cp\u003eIn conclusion, this study harnesses the power of AI technology to provide an unprecedented, quantitative characterization of fundus tessellation in a defined occupational cohort. We validate FTD as a sensitive imaging biomarker for high myopia and delineate its significant associations with occupational specialization, critical near-work behavior, and systemic inflammation. These insights underscore the necessity for targeted occupational health strategies and lifestyle modifications for at-risk groups. The integration of AI-based FTD quantification into large-scale screening paradigms holds immense promise for the early identification, risk stratification, and timely management of individuals along the sight-threatening pathway of myopic chorioretinal degeneration.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, this prospective cluster-randomized study demonstrates the significant utility of deep learning-based AI systems in quantifying fundus tessellation density and elucidating its association with occupational factors in a teacher cohort. Our findings reveal that occupational specificity, particularly teaching literature subjects, and behavioral factors including prolonged near work at shorter distances (30 cm) are independently associated with myopia progression and increased FTD. Regional analysis further identifies the nasal macular quadrant as particularly vulnerable to tessellation changes during axial elongation. The strong discriminative performance of both overall FTD (AUC\u0026thinsp;=\u0026thinsp;0.7788) and nasal FTD (AUC\u0026thinsp;=\u0026thinsp;0.7631) in detecting high myopia supports their potential as reliable screening biomarkers. Additionally, the elevated systemic immune-inflammatory markers (NLR and PLR) in high myopia groups suggest a potential role of systemic inflammation in myopia pathogenesis. These findings highlight the value of AI-assisted fundus imaging in occupational health screening and underscore the importance of developing targeted intervention strategies for high-risk professional groups. Future longitudinal studies incorporating axial length measurement and mechanistic investigations into inflammatory pathways will further enhance our understanding of occupation-related myopic progression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to Dr. Yu Hong and the Model Worker Health Management Center of Beijing Rehabilitation Hospital for their valuable data support. We thank EVision Technology (Beijing) Co. Ltd. for their EVisionAI software for data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Science and Technology Development Special Fund, Beijing Rehabilitation Hospital, Capital Medical University (grant numbers 2021-071).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll investigations were conducted in accordance with the Declaration of Helsinki and approved by the institutional review board of Beijing Rehabilitation Hospital Affiliated to Capital Medical University (No. 2021bkky-105). Each participant provided informed consent. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are available within the paper and its Supplementary Information. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConceptualization:\u003c/em\u003e Z. X., H. L., and H. Y..\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInvestigation:\u003c/em\u003e Z. X., H. L., H. W., M. S., G. L., C. Y., L. L., M. L., J. S., Y. R., and Y. G..\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Curation and Formal Analysis: \u003c/em\u003eZ. X. and H. L..\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eProject Administration: \u003c/em\u003eZ. X., H. L., Y. Z., H. Y., and Y. L..\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWriting – original draft:\u003c/em\u003e Z. 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DOI: 10.18240/ijo.2023.11.01.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the supplementary files section\u003c/p\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, fundus image, fundus tessellated density, teachers, deep learning, near work, myopia, systemic immune-inflammation index","lastPublishedDoi":"10.21203/rs.3.rs-8229056/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8229056/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThis study was to investigate the practical application of artificial intelligence (AI)-based fundus screening system for ophthalmic health management in the teacher population, specifically aiming to explore the etiology and potential pathogenesis of high myopia-associated fundus lesion progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA randomized of primary and secondary school teachers from Beijing, provided color fundus images and health examination data. The study innovatively employed AI for assisted diagnosis and analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean spherical equivalent (SE) was \u0026minus;\u0026thinsp;2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94 D. Univariate analysis identified significant correlations between SE and factors such as age, teaching subject, and near work duration. Quantitative analysis revealed that fundus tessellated density (FTD) was highest in the nasal macular quadrant. Both overall and nasal FTD within the 3 mm macular region effectively discriminated high myopia (SE \u0026le; -6.00 D), with area under the curve values of 0.7788 and 0.7631, respectively. Furthermore, teachers of literature subjects exhibited significantly lower SE and higher FTD, while prolonged near work (\u0026ge;\u0026thinsp;1 hour) at 30 cm was associated with increased FTD. Finally, FTD positively correlated with systemic immune-inflammatory indices (neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio), which were elevated in high myopia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study demonstrates the characteristics of the progression of myopia and fundus lesions in the teacher population through AI-based fundus screening system. AI-quantified FTD is a valuable biomarker for high myopia, is influenced by occupational near work, and is associated with a systemic pro-inflammatory state.\u003c/p\u003e","manuscriptTitle":"AI-Based Fundus Screening of Teachers: Fundus Tessellated Density as a Myopia Biomarker and Occupational Associations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 06:23:16","doi":"10.21203/rs.3.rs-8229056/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-15T09:32:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-14T07:00:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160429973312155364068717033908552976212","date":"2026-03-14T05:57:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135158945117545943781546414247427251798","date":"2026-03-11T01:33:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T08:42:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7316236768384883946271694383782215276","date":"2026-02-11T11:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-09T04:39:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-29T11:33:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-29T11:32:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-28T09:39:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9d348200-a9cf-4f51-9e60-01716c4ace2c","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T13:39:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 06:23:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8229056","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8229056","identity":"rs-8229056","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}