Prediction of long-term uncorrected distance visual acuity in surgically SMILE corrected myopic eyes using machine learning.

ABSTRACT:

Background

This study aimed to create machine learning (ML) models to predict the long-term uncorrected distance visual acuity (UDVA) in myopic eyes corrected by small incision lenticule extraction (SMILE).

Methods

This was a retrospective cohort study conducted in Zhongshan Ophthalmic Center, Sun Yat-sen University. Participants who underwent SMILE between 2012 and 2016 were invited for the final follow-up examinations in 2019. Medical records and surgical parameter data were collected for analysis. Multicollinearity analysis and feature importance ranking were used to select the most predictive features. The following algorithms were used: least absolute shrinkage and selection operator, random forest, extremely randomised regression trees (extraTrees), gradient boosting m

SUBMITTER: Yang X 

PROVIDER: S-EPMC12481272 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature