Retinograd-AI: An Open-Source Automated Fundus Autofluorescence Retinal Image Gradability Assessment for Inherited Retinal Diseases.

ABSTRACT:

Purpose

To develop an automated system for assessing the quality of fundus autofluorescence (FAF) images in patients with inherited retinal diseases (IRDs).

Design

A retrospective study of imaging data.

Participants

Patients with a confirmed molecular diagnosis of IRD who have undergone FAF imaging at Moorfields Eye Hospital.

Methods

A dataset of 2445 FAF images from patients with IRD was marked by 3 expert graders as either gradable (acceptable quality) or ungradable (poor quality), following a strict grading protocol. This dataset was used to train an artificial intelligence (AI) algorithm, Retinograd-AI, which was then applied to predict the gradability label of our entire dataset of 136 631 FAF images.

Main outcome measures

Fundus autofluorescence gr

SUBMITTER: Naik G 

PROVIDER: S-EPMC12309597 | biostudies-literature | 2025 Nov-Dec

REPOSITORIES: biostudies-literature