FFA-Lens: Lesion detection tool for chronic ocular diseases in Fluorescein angiography images

Abstract

Fundus Fluorescein Angiography (FFA) is an important invasive diagnostic tool employed in ophthalmology for the assessment of retinal and choroidal blood flow, components situated at the rear of the eye. Identifying lesions within FFA images, indicative of anomalies in the retina and choroid visualized through the injection of a fluorescein dye, is a critical step in diagnosing and managing chronic ocular diseases. Such lesions often hint at a range of eye disorders, including but not limited to diabetic retinopathy, age-related macular degeneration, and retinal vein occlusions. In conventional practice, these images are interpreted manually by a skilled ophthalmologist. Nonetheless, due to the intricate nature of these images, the manual process is often time-consuming and subject to individual interpretation. The advent of advanced technologies has instigated the development of automated lesion detection techniques, thereby alleviating these constraints.In this study, we introduce FFA-Lens, a web-based application engineered to detect 25 prevalent lesions in FFA images, serving as a crucial resource in diagnosing and treating chronic ocular diseases. Notably, FFA-Lens has demonstrated remarkable precision and recall values exceeding 0.8 in lesion detection, emphasizing its significant potential. By enabling early detection of ocular conditions, automating lesion identification, and enhancing diagnostic efficiency, FFA-Lens can substantially improve clinical disease management strategies.

Keywords

• Fundus fluorescein angiography
• YOLO
• Automatic detection
• CNN
• Deep learning
• OCT