Enhancing Vault Prediction and ICL Sizing Through Advanced Machine Learning Models.

Purpose: To use artificial intelligence (AI) technology to accurately predict vault and Implantable Collamer Lens (ICL) size.

Methods: The methodology focused on enhancing predictive capabilities through the fusion of machine-learning algorithms. Specifically, AdaBoost, Random Forest, Decision Tree, Support Vector Regression, LightGBM, and XGBoost were integrated into a majority-vote model.

Predictability comparison of sizing parameters for postoperative vault after implantable Collamer lens implantation.

Purpose: This study aims to assess the accuracy of three parameters (white-to-white distance [WTW], angle-to-angle [ATA], and sulcus-to-sulcus [STS]) in predicting postoperative vault and to formulate an optimized predictive model.

Methods: In this retrospective study, a cohort of 465 patients (comprising 769 eyes) who underwent the implantation of the V4c implantable Collamer lens with a central port (ICL) for myopia correction was examined. Least absolute shrinkage and selection operator (LASSO) regression and classification models were used to predict postoperative vault.