# DME-RWKV: An Interpretable Multimodal Deep Learning Framework for Predicting Anti-VEGF Response in Diabetic Macular Edema

## TL;DR

This paper introduces DME-RWKV, an AI model that predicts how well patients with diabetic macular edema will respond to anti-VEGF treatment using eye imaging data.

## Contribution

The novel DME-RWKV model combines OCT and UWF imaging with interpretable deep learning techniques for improved treatment prediction and biomarker analysis.

## Key findings

- DME-RWKV achieved a Dice coefficient of 71.91 ± 8.50% for OCT biomarker segmentation.
- The model demonstrated an AUC of 84.36% for predicting anti-VEGF treatment response.
- The model outperformed state-of-the-art methods in both segmentation and prediction tasks.

## Abstract

Diabetic macular edema (DME) is a leading cause of vision loss, and predicting patients’ response to anti-vascular endothelial growth factor (anti-VEGF) therapy remains a clinical challenge. In this study, we developed an interpretable deep learning model for treatment prediction and biomarker analysis. We retrospectively analyzed 402 eyes from 371 patients with DME. The proposed DME-Receptance Weighted Key Value (RWKV) integrates optical coherence tomography (OCT) and ultra-widefield (UWF) imaging using Causal Attention Learning (CAL), curriculum learning, and global completion (GC) loss to enhance microlesion detection and structural consistency. The model achieved a Dice coefficient of 71.91 ± 8.50% for OCT biomarker segmentation and an AUC of 84.36% for predicting anti-VEGF response, outperforming state-of-the-art methods. By mimicking clinical reasoning with multimodal integration, DME-RWKV demonstrated strong interpretability and robustness, providing a promising AI framework for precise and explainable prediction of anti-VEGF treatment outcomes in DME.