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Updated: Sep 13, 2025
Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
Published on: March 11, 2016
Retinograd-AI: An Open-Source Automated Fundus Autofluorescence Retinal Image Gradability Assessment for Inherited
Gunjan Naik1,2,3, Saoud Al-Khuzaei4,5, Ismail Moghul1,2,3
1Faculty of Brain Sciences, UCL Institute of Ophthalmology, London, United Kingdom.
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 gradability of FAF images as predicted and validated against human assessment.
Results:
Retinograd-AI achieves 91% accuracy on our held-out dataset of 133 images with an area under the receiver operator characteristic curve of 0.94, indicating high performance in distinguishing between gradable and ungradable images. Applying Retinograd-AI to our entire dataset, a small but significant positive association of gradability with age was found (ß = 0.002, P < 0.001). Excluding X-linked conditions, 77.1% of images were rated as gradable in men and 82.3% in women (odds ratio = 1.43, P < 0.001). By genotype, from the 30 most common genetic diagnoses in our dataset, the highest proportion of gradable images was in patients with disease-causing variants in PRPH2 (93.1%), while the lowest was in RDH12 (27.1%). Applying Retinograd-AI to filter images improved the accuracy of a gene prediction classifier from 33.8% to 68.9%. Retinograd-AI is open-sourced and available at https://github.com/Eye2Gene/retinograd-ai.
Conclusions:
Retinograd-AI is an open-source AI model for automated retinal image quality assessment of FAF images in IRDs. Automated gradability assessment through Retinograd-AI enables large-scale analysis of retinal images and the development of robust analysis pipelines. Quality assessment is essential for the deployment of AI algorithms, such as Eye2Gene, into clinical settings. Due to the diverse nature of IRD pathologies, Retinograd-AI will be extended to other conditions, either in its current form or through transfer learning and fine-tuning.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
