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Farjallah, E.* ; Shamieh, S.E.* ; Rezaei, R.* ; Herrmann, P.* ; Künzel, S.H.* ; Holz, F.G.* ; Albarqouni, S.

Affordable deep learning for diagnosing inherited and common retinal diseases via color fundus photography.

In: (Ophthalmic Medical Image Analysis). Berlin [u.a.]: Springer, 2025. 83-93 (Lect. Notes Comput. Sc. ; 15188 LNCS)
DOI
Retinal diseases are a significant global health concern, often leading to severe vision impairment or blindness if not diagnosed and treated promptly. Classifying inherited retinal diseases (IRDs) is particularly challenging, typically requiring genetic analysis and expert ophthalmologists. Developing automatic deep learning models for their classification is crucial, especially for regions with low-resource settings. This work focuses on diagnosing IRDs and other common retinal conditions using color fundus photographs (CFPs), leveraging transformative advances in deep learning. Our approach utilizes a ResNet18 model trained on CFPs from seven diverse, multi-institute sources, aiming to classify 21 retinal diseases, including 13 IRDs. This is the highest number of IRD classes addressed by a single model using color fundus photographs to date. The model achieved an impressive F1 score of 0.86 for Retinitis Pigmentosa (RP), demonstrating its capability for broad diagnostic use. The results highlight the feasibility of employing deep learning for IRD detection, a task that traditionally relies on expensive and time-consuming genetic testing. The inclusion of a diverse dataset ensures robust performance and generalizability across various demographics. Activation map analysis confirms the model’s accuracy in identifying disease patterns. These promising results mark a significant step towards more accessible and efficient retinal disease diagnosis through deep learning technology.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Deep Learning ; Inherited Retinal Diseases ; Retinal Imaging
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Ophthalmic Medical Image Analysis
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15188 LNCS, Heft: , Seiten: 83-93 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)