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<p>Retinal artery occlusion (RAO) is a sight-threatening condition that requires prompt diagnosis to prevent irreversible vision loss. This study presents an innovative AI-driven approach for RAO detection from fundus images, marking the first application of deep learning for this purpose. Using a self-supervised learning (SSL) framework with SimCLR, our model addresses the challenge of limited labeled RAO data. The ResNet50 model pretrained with SimCLR demonstrated high diagnostic accuracy, achieving areas under the receiver operating characteristic curve (AUC) of 0.924 and 0.988 on two external validation datasets, highlighting its robustness and generalizability in RAO detection. To enhance transparency in clinical AI, we incorporated a multimodal interpretability approach using a ChatGPT-4-based AI chatbot. This chatbot, combined with Grad-CAM visualizations, provides detailed clinical explanations of the model’s predictions, emphasizing key RAO features such as retinal whitening and cherry-red spots. This multimodal interpretability framework improves clinicians’ understanding of the model’s decision-making process, facilitating clinical adoption and trust. By automating RAO detection, this AI model serves as a valuable tool for the early identification of ocular and systemic vascular risks, enabling timely intervention. These findings highlight the potential of fundus imaging for RAO detection and broader cardiovascular risk assessment, advancing AI’s role in predictive healthcare.</p> Graphical Abstract <p></p>

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Automated detection of retinal artery occlusion in fundus photography via self-supervised deep learning and multimodal interpretability using a multimodal AI chatbot

  • 期刊论文

  • 发表日期：

    2025年3月31日

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  • Sun Young Ryu,
  • Joon Yul Choi,
  • Tae Keun Yoo

摘要

Retinal artery occlusion (RAO) is a sight-threatening condition that requires prompt diagnosis to prevent irreversible vision loss. This study presents an innovative AI-driven approach for RAO detection from fundus images, marking the first application of deep learning for this purpose. Using a self-supervised learning (SSL) framework with SimCLR, our model addresses the challenge of limited labeled RAO data. The ResNet50 model pretrained with SimCLR demonstrated high diagnostic accuracy, achieving areas under the receiver operating characteristic curve (AUC) of 0.924 and 0.988 on two external validation datasets, highlighting its robustness and generalizability in RAO detection. To enhance transparency in clinical AI, we incorporated a multimodal interpretability approach using a ChatGPT-4-based AI chatbot. This chatbot, combined with Grad-CAM visualizations, provides detailed clinical explanations of the model’s predictions, emphasizing key RAO features such as retinal whitening and cherry-red spots. This multimodal interpretability framework improves clinicians’ understanding of the model’s decision-making process, facilitating clinical adoption and trust. By automating RAO detection, this AI model serves as a valuable tool for the early identification of ocular and systemic vascular risks, enabling timely intervention. These findings highlight the potential of fundus imaging for RAO detection and broader cardiovascular risk assessment, advancing AI’s role in predictive healthcare.

Graphical Abstract