A clinician aligned vision language framework for stepwise interpretation in fundus fluorescein angiography.

Authors

Zichang Su, Xiaocong Liu, Bingtao Guan, An Shao, Xindi Liu, Yan Yan, Ziyao Luo, Zhikang Li, Yufeng Xu, Jian Wu, Xiaoling Huang, Juan Ye

Published in

NPJ digital medicine. Jul 12, 2026. Epub Jul 12, 2026.

Abstract

Fluorescein fundus angiography (FFA) is essential for diagnosing retinal vascular diseases, yet its interpretation is expertise-intensive. Here, we present Clin-FFA-VLM, a multimodal vision-language framework that mirrors retina specialists' cognitive workflow by decomposing FFA interpretation into three stages: lesion-aware visual perception, clinical report generation, and diagnostic decision support. Trained and tested on a multi-center dataset of 13,178 FFA images with expert label, 21,717 FFA images with 1790 clinical reports and diagnosis across 7 retinal diseases, Clin-FFA-VLM achieves an F1 of 0.834 for lesion detection, an entity-level F1 of 0.73 for report generation, and a diagnostic F1 of 0.77 by jointly reasoning over images and self-generated reports. External validation across two independent hospitals confirmed its generalizability (F1 of 0.78 and 0.70). In a prospective reader study with 200 FFA cases, Clin-FFA-VLM significantly improved diagnostic accuracy for medical students and residents (p < 0.05), bridging the gap between automated systems and clinical practice.

PMID:
42437856
Bibliographic data and abstract were imported from PubMed on 13 Jul 2026.

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