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

ABSTRACT:

Background

Diabetic retinopathy is a leading cause of vision impairment, often progressing to neovascular glaucoma. Early detection of neovascularisation of the iris (NVI) is crucial for timely intervention. Traditional diagnostic methods, such as slit-lamp examination, have limitations in identifying early-stage NVI. This study presents a deep learning-based automated approach for analysing iris fluorescein angiography (IFA) images to detect and quantify peripupillary leakage, a key indicator of NVI.

Methods

A dataset of 2,449 IFA images was used to train a YOLOv8n-based segmentation model for precise pupil localisation. A leakage circularity detection algorithm was developed to quantify peripupillary fluorescein leakage. The algorithm's performance was evaluated using an i

SUBMITTER: Zhu Y 

PROVIDER: S-EPMC12780619 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature