An explainable machine learning framework for accurate prediction of postoperative anterior chamber depth in highly myopic cataract surgery.

Purpose: To develop and validate machine learning models for predicting postoperative anterior chamber depth (ACD) in highly myopic cataract patients based on preoperative biometric parameters. Methods: This prospective study enrolled 203 eyes of 127 highly myopic patients who underwent phacoemulsification and intraocular lens (IOL) implantation between January 2024 and December 2025. Ocular biometric parameters were measured preoperatively and at 3 months postoperatively. A dual feature selection strategy combining Least Absolute Shrinkage and Selection Operator (LASSO) regression and Boruta algorithm was employed to identify important predictors of postoperative ACD in these samples. We compared five machine learning algorithms and evaluated their performance using the coefficient of determination ( Results: Six predictors were identified for model construction: axial length ACD/lens thickness ratio (ACD/LT), white-to-white distance (WTW), ACD at 90° (ACD90), horizontal position angle, and ACD + LT/2. Among all models, Random Forest algorithm demonstrated the best predictive performance, achieving an Conclusion: This study successfully developed and validated an explainable RF-based machine learning model for the prediction of postoperative ACD in highly myopic cataract patients, which supports more reliable IOL power calculation and offers a practical tool for optimizing surgical planning in highly myopic eyes.