Predicting branch retinal vein occlusion development using multimodal deep learning and pre-onset fundus hemisection images.

ABSTRACT: Branch retinal vein occlusion (BRVO) is a leading cause of visual impairment in working-age individuals, though predicting its occurrence from retinal vascular features alone remains challenging. We developed a deep learning model to predict BRVO based on pre-onset, metadata-matched fundus hemisection images. This retrospective cohort study included patients diagnosed with unilateral BRVO from two Korean tertiary centers (2005-2023), using hemisection fundus images from 27 BRVO-affected eyes paired with 81 unaffected hemisections (27 counter and 54 contralateral) for training. A U-net model segmented retinal optic discs and blood vessels (BVs), dividing them into upper and lower halves labeled for BRVO occurrence. Both unimodal models (using either fundus or BV images) and a BV-enhanced mu

SUBMITTER: Choi EY 

PROVIDER: S-EPMC11751167 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature