Automated method for quantitative analysis of iris fluorescein angiography based on machine learning

The deep learning-based approach provides superior consistency and accuracy in quantifying peripupillary fluorescein leakage compared to manual expert annotations. While human experts demonstrated slightly higher spatial precision, the algorithm significantly reduces variability and subjectivity in leakage quantification. This automated method has the potential to enhance early detection of NVI, improve clinical workflow efficiency, and assist ophthalmologists in diagnosing DR. Further optimization will focus on refining spatial segmentation accuracy.