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<p>Retinal vein occlusion (RVO) is a chronic retinal vascular disease that often requires repeated anti-VEGF injections and long-term follow-up. However, predicting treatment responses across different follow-up timepoints remains clinically challenging. To address this issue, we developed an AI system integrating generative adversarial networks (GANs), UNet + +, and ResNet-101 to generate post-treatment OCT and fundus images and support clinical decision-making. A total of 2304 OCT and 576 fundus images from 576 RVO patients were collected at baseline and at weeks 4, 12, and 24 after treatment. The generated images demonstrated favorable visual quality, as evaluated by mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). The system further quantified lesion areas and predicted retreatment needs, achieving average AUCs of 0.854 and 0.744 across six models in the internal and external test datasets, respectively. In the reader study, the AI system achieved higher predictive accuracy than retinal specialists while substantially reducing image interpretation time. Clinicians’ predictive performance also improved with AI assistance.</p>

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Treatment effects prediction and clinical decision-making system for retinal vein occlusion by artificial intelligence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月9日

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  • He-Yan Li,
  • Jin-Jie Guo,
  • Guo-Jiao Song,
  • Kai Zhang,
  • Kai Jin,
  • Wen-Bin Wei,
  • Lei Shao

摘要

Retinal vein occlusion (RVO) is a chronic retinal vascular disease that often requires repeated anti-VEGF injections and long-term follow-up. However, predicting treatment responses across different follow-up timepoints remains clinically challenging. To address this issue, we developed an AI system integrating generative adversarial networks (GANs), UNet + +, and ResNet-101 to generate post-treatment OCT and fundus images and support clinical decision-making. A total of 2304 OCT and 576 fundus images from 576 RVO patients were collected at baseline and at weeks 4, 12, and 24 after treatment. The generated images demonstrated favorable visual quality, as evaluated by mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). The system further quantified lesion areas and predicted retreatment needs, achieving average AUCs of 0.854 and 0.744 across six models in the internal and external test datasets, respectively. In the reader study, the AI system achieved higher predictive accuracy than retinal specialists while substantially reducing image interpretation time. Clinicians’ predictive performance also improved with AI assistance.