Compact Vision-Language Models Enable Efficient and Interpretable Automated OCT Analysis Through Layer Specific Multimodal Learning.

ABSTRACT: Translating the intricate anatomical signatures of retinal disease from OCT B-scans into clear, accurate clinical narratives demands AI models that seamlessly fuse visual features with domain expertise. We curated a multimodal dataset of 40,000 OCT B-scans from public repositories and private clinical cohorts, each paired with expert validated summaries spanning six conditions: diabetic macular edema, diabetic retinopathy, geographic atrophy, drusen, choroidal neovascularization, and healthy retina. We introduce LO-VLM, a compact (247M parameter) vision-language model (VLM) that infuses anatomical guidance into both encoder and decoder for free form summary generation and multiclass disease classification. Benchmarking against state-of-the-art RetinaVLM, LLaVA-Med, and a ViT vision only mo

SUBMITTER: Haghighi T 

PROVIDER: S-EPMC12363835 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature