Purpose: To develop an automated system for assessing the quality of Fundus Autofluorescence (FAF) images in patients with inherited retinal diseases (IRD). Methods: We annotated a dataset of 2445 FAF images from patients with Inherited Retinal Dystrophies which were assessed by three different expert graders. Graders marked images as either gradable (acceptable quality) or ungradable (poor quality), following a strict grading protocol. This dataset was used to train a Convolutional Neural Network (CNN) classification model to predict the gradability label of FAF images. Results: Retinograd-AI achieves a performance of 91% accuracy on our held-out dataset of 133 images with an Area Under the Receiver Operator Characteristic (AUROC) of 0.94, indicating high performance in distinguishing between gradable and ungradable images. Applying Retinograd-AI to our full internal dataset, the highest proportion of gradable images was found in the 30-50 years age group, where 84.3% of images were rated as gradable, while the lowest was in 0-15 year olds, where only 45.2% of images were rated as gradable. 83.4% of images from male patients were rated as gradable, and 90.6% of images from female patients. By genotype, from the 30 most common genetic diagnoses, the highest proportion of gradable images was in patients with disease causing variants in PRPH2 (93.9%), while the lowest was RDH12 (28.6%). Eye2Gene single-image gene classification top-5 accuracy on images rated by Retinograd-AI was 69.2%, while top-5 accuracy on images rated as ungradable was 39.0%. Conclusions: Retinograd-AI is the first 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, which is an essential part of developing good analysis pipelines, and real-time quality assessment, which is essential for deployment of AI algorithms, such as Eye2Gene, into clinical settings. Due to the diverse nature of IRD pathologies, Retinograd-AI may also be applicable to FAF imaging for other conditions, either in its current form or through transfer learning and fine-tuning. Retinograd-AI is open-sourced, and the source code and network weights are available under an MIT licence on GitHub at https://github.com/eye2gene/retinograd-ai.

The authors have declared no competing interest.

This work is primarily funded by a NIHR AI Award (AI_AWARD02488) which supported NP, WAW, MM, KB, SD and SM. The research was also supported by a grant from the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. NP was also previously funded by Moorfields Eye Charity Career Development Award (R190031A). OAM is supported by the Wellcome Trust (206619/Z/17/Z). SA is supported by a scholarship from Qatar National Research Fund (GSRA6-1-0329-19010).This project was also supported by a generous donation by Stephen and Elizabeth Archer in memory of Marion Woods. The hardware used for analysis was supported by the BRC Challenge Fund (BRC3_027).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This research was approved by the IRB and the UK Health Research Authority Research Ethics Committee (REC) reference (22/WA/0049) "Eye2Gene: accelerating the diagnosis of inherited retinal diseases" Integrated Research Application System (IRAS) (project ID: 242050). All research adhered to the tenets of the Declaration of Helsinki.  

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Unfortunately we are not able to share the data used in this study however code and network weights are available online at https://github.com/eye2gene/retinograd-ai

https://github.com/eye2gene/retinograd-ai