Fast and accurate visual acuity prediction based on optical aberrations and machine learning.

ABSTRACT: In this work, we propose three machine learning-based methods for predicting visual acuity (VA). Two methods utilize regression trees (LSBoost and XGBoost), and the third employs a neural network that classifies simulated aberrated optotypes as "recognized" or "unrecognized". The overall VA is estimated by replicating the clinical procedure in which the subject reads optotypes and the VA is determined based on their responses. Here, the neural network acts as a substitute for the subject. Data were collected from a clinical trial involving 135 subjects providing for each sample 36 Zernike coefficients, amplitudes of accommodation, age, and VA values. Evaluation of the regression tree models demonstrates that LSBoost outperformes XGBoost in prediction accuracy, especially when incorporating

SUBMITTER: Sierra A 

PROVIDER: S-EPMC12717265 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature