Classification of Inherited Retinal Diseases Using Artificial Intelligence Models for Fundus Autofluorescence and Ultrawide Retinal Images
Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine
Background/Objectives Inherited retinal diseases (IRDs) are a leading cause of blindness in working‐age adults. Although artificial intelligence (AI) shows potential for disease classification, progress is limited by small datasets, reliance on labelled data and limited integration of multiple imaging modalities. RETFound, a foundation model pretrained on over 900,000 fundus photographs, may address these limitations. While RETFound is based on the transformer architecture, convolutional neural networks (CNNs), such as ResNet and EfficientNet_B0, have also demonstrated strong performance in classifying retinal diseases. This study adapted RETFound and CNNs for analysing fundus autofluorescence (FAF) and pseudocolour ultra‐widefield (UWF) images, establishing a framework to classify IRDs. sec-0001 Subjects/Methods Deidentified FAF and UWF images were obtained from controls and patients with Best disease, rod‐cone dystrophy, Stargardt disease and choroideremia. Images underwent preprocessing and were used to fine‐tune RETFound. Model performance was compared with classical machine learning algorithms (logistic regression, support vector machine, gradient boosting and random forest) and deep learning architectures (ResNet18, ResNet50, vision transformer, EfficientNet_B0 and ConvNeXt‐Tiny) pretrained on ImageNet. sec-0002 Results The fine‐tuned RETFound model achieved an accuracy of 0.815, with the best performance for rod‐cone dystrophy (F1 = 0.820). RETFound outperformed classi
Abstract
Background/Objectives Inherited retinal diseases (IRDs) are a leading cause of blindness in working‐age adults. Although artificial intelligence (AI) shows potential for disease classification, progress is limited by small datasets, reliance on labelled data and limited integration of multiple imaging modalities. RETFound, a foundation model pretrained on over 900,000 fundus photographs, may address these limitations. While RETFound is based on the transformer architecture, convolutional neural networks (CNNs), such as ResNet and EfficientNet_B0, have also demonstrated strong performance in classifying retinal diseases. This study adapted RETFound and CNNs for analysing fundus autofluorescence (FAF) and pseudocolour ultra‐widefield (UWF) images, establishing a framework to classify IRDs. sec-0001 Subjects/Methods Deidentified FAF and UWF images were obtained from controls and patients with Best disease, rod‐cone dystrophy, Stargardt disease and choroideremia. Images underwent preprocessing and were used to fine‐tune RETFound. Model performance was compared with classical machine learning algorithms (logistic regression, support vector machine, gradient boosting and random forest) and deep learning architectures (ResNet18, ResNet50, vision transformer, EfficientNet_B0 and ConvNeXt‐Tiny) pretrained on ImageNet. sec-0002 Results The fine‐tuned RETFound model achieved an accuracy of 0.815, with the best performance for rod‐cone dystrophy (F1 = 0.820). RETFound outperformed classical machine learning models and a vision transformer pretrained on ImageNet. ResNet18 achieved a weighted F1 of 0.839 and demonstrated the best classification performance for Stargardt disease and choroideremia (F1 0.821 and 0.728, respectively). ResNet50 achieved a weighted F1 of 0.825 and demonstrated the best classification performance for normal retinas and Best disease (F1 0.945 and 0.616, respectively). ResNet architectures achieved the best performance overall. sec-0003 Conclusions ResNet architectures and fine‐tuned RETFound both demonstrated strong accuracy in classifying IRD classes, demonstrating potential for clinical application and deployment in eye care settings as tools for IRD diagnosis, triage and management. sec-0004