Abstract:AIM: To investigate the prevalence of pterygium in the southeast coastal area, analyze associated risk factors, and develop a multimodal machine learning predictive model.
METHODS:A cross-sectional epidemiological study. A random sampling of the resident population of the Zhoushan Islands was performed. Data were collected through on-site ophthalmic examinations and epidemiological surveys. Univariate and multivariate analyses were performed to identify risk factors associated with pterygium. A multimodal artificial intelligence machine learning model was constructed to integrate and analyze both structured and unstructured data for predictive modeling.
RESULTS:A total of 1 842 participants were enrolled in this study, including 926 males(50.27%)and 916 females(49.73%), with a mean age of 48.62±10.35 y. A total of 644 patients(34.96%), including 323 males(50.15%)and 321 females(49.84%)were diagnosed with pterygium, including 502 cases(77.95%)were unilateral, and 142 cases(22.05%)were bilateral. There were 1 198 cases without pterygium, including 603 males(50.33%)and 595 females(49.67%). Occupation, daily outdoor sunlight exposure duration, meibomian gland orifice obstruction, age, smoking, and alcohol consumption were identified as significant risk factors for pterygium. Occupation and daily outdoor sunlight exposure duration were the most potent risk factors(OR=6.125, P<0.001; OR=5.348, P<0.001, respectively), followed by age >40 y(OR=5.295, P<0.001)and meibomian gland orifice obstruction(OR=5.248, P<0.001). The multimodal machine learning model demonstrated good predictive performance, as evidenced by the receiver operating characteristic(ROC)curve, precision-recall(PR)curve, and confusion matrix, all indicating high predictive value, AUC=0.86, AP=0.77.
CONCLUSION: Fishermen and farmers aged over 40 y with more than 4 h of daily sunlight exposure are at high risk for pterygium in the southeast coastal area. Meibomian gland dysfunction is correlated with corneal invasion of pterygium and may facilitate its progression; therefore, active health education and targeted intervention are recommended. The multimodal machine learning model provides accurate and reliable predictive capability for pterygium risk, which may facilitate risk stratification and early warning for pterygium in southeast coastal populations.
