A systematic review of advances in AI-assisted analysis of fundus fluorescein angiography (FFA) images : from detection to report generation

Abstract

Fundus fluorescein angiography (FFA) serves as the current gold standard for visualizing retinal vasculature and detecting various fundus diseases, but its interpretation is labor-intensive and requires much expertise from ophthalmologists. The medical application of artificial intelligence (AI), especially deep learning and machine learning, has revolutionized the field of automatic FFA image analysis, leading to the rapid advancements in AI-assisted lesion detection, diagnosis, and report generation. This review examined studies in PubMed, Web of Science, and Google Scholar databases from January 2019 to August 2024, with a total of 23 articles incorporated. By integrating current research findings, this review highlights crucial breakthroughs in AI-assisted FFA analysis and explores their potential implications for ophthalmic clinical practice. These advances in AI-assisted FFA analysis have shown promising results in improving diagnostic accuracy and workflow efficiency. However, further research is needed to enhance model transparency and ensure robust performance across diverse populations. Challenges such as data privacy and technical infrastructure remain for broader clinical applications.