Development and validation to predict visual acuity and keratometry two years after corneal crosslinking with progressive keratoconus by machine learning.

ABSTRACT:

Purpose

To explore and validate the utility of machine learning (ML) methods using a limited sample size to predict changes in visual acuity and keratometry 2 years following corneal crosslinking (CXL) for progressive keratoconus.

Methods

The study included all consecutive patients with progressive keratoconus who underwent CXL from July 2014 to December 2020, with a 2 year follow-up period before July 2022 to develop the model. Variables collected included patient demographics, visual acuity, spherical equivalence, and Pentacam parameters. Available case data were divided into training and testing data sets. Three ML models were evaluated based on their performance in predicting case corrected distance visual acuity (CDVA) and maximum keratometry (K_max) changes c

SUBMITTER: Liu Y 

PROVIDER: S-EPMC10393251 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature