AI-based prediction of best-corrected visual acuity in patients with multiple retinal diseases using multimodal medical imaging.

ABSTRACT:

Background/aims

This study evaluated the performance of artificial intelligence (AI) algorithms in predicting best-corrected visual acuity (BCVA) for patients with multiple retinal diseases, using multimodal medical imaging including macular optical coherence tomography (OCT), optic disc OCT and fundus images. The goal was to enhance clinical BCVA evaluation efficiency and precision.

Methods

A retrospective study used data from 2545 patients (4028 eyes) for training, 896 (1006 eyes) for testing and 196 (200 eyes) for internal validation, with an external prospective dataset of 741 patients (1381 eyes). Single-modality analyses employed different backbone networks and feature fusion methods, while multimodal fusion combined modalities using average aggregation, concatenation/

SUBMITTER: Dong L 

PROVIDER: S-EPMC12911652 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature