Corneal Endothelium and Tear Film Metrics Enhance the Accuracy of Machine Learning Prediction in Implantable Collamer Lens Surgery Outcomes

PURPOSE: To develop and evaluate a machine learning (ML) model to predict postoperative vault and residual refractive error following Implantable Collamer Lens (ICL) implantation by incorporating corneal functional parameters, and to investigate their influence on prediction accuracy. METHODS: ), and Wilcoxon signed-rank tests. RESULTS: < .05). Endothelium metrics also improved sphere prediction, with numerically lower MedAE and a higher percentage of prediction errors within ±0.25, ±0.50, and ±0.75 diopters. CONCLUSIONS: In the MPA-SVM ICL prediction model, corneal endothelium metrics significantly improves vault prediction and, when combined with tear film quality parameters, enhances cylinder prediction after ICL implantation. This enhanced model offers ophthalmologists a valuable tool for improving the safety and planning of ICL procedures.