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

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 (generative adversarial networks), UNet + +, and ResNet-101 to generate post-treatment OCT and fundus images and support clinical decision-making.

The generated images demonstrated favorable visual quality, as evaluated by mean absolute error (mean absolute error), peak signal-to-noise ratio (peak signal-to-noise ratio), and structural similarity index measure (structural similarity index measure).