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

Yan Liu1,2, Xieyang Xu1,2, Jiaying Zhang1,2

  • 1Department of Ophthalmology, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.

Bioengineering (Basel, Switzerland)
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Summary

Predicting diabetic macular edema (DME) treatment response is challenging. A new AI model integrating OCT and UWF imaging accurately predicts patient outcomes for anti-VEGF therapy, offering explainable insights.

Keywords:
causal attentioncurriculum learningdiabetic macular edemaepiretinal membranemultimodal deep learningoptical coherence tomographyultra-widefield imaging

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic macular edema (DME) is a primary cause of vision loss.
  • Predicting patient response to anti-vascular endothelial growth factor (anti-VEGF) therapy for DME is a significant clinical challenge.

Purpose of the Study:

  • To develop an interpretable deep learning model for predicting anti-VEGF treatment response in DME patients.
  • To analyze biomarkers and enhance microlesion detection using multimodal imaging data.

Main Methods:

  • Retrospective analysis of 402 eyes from 371 DME patients.
  • Development of the DME-Receptance Weighted Key Value (RWKV) model integrating optical coherence tomography (OCT) and ultra-widefield (UWF) imaging.
  • Utilized Causal Attention Learning (CAL), curriculum learning, and global completion (GC) loss for enhanced analysis.

Main Results:

  • Achieved a Dice coefficient of 71.91 ± 8.50% for OCT biomarker segmentation.
  • Obtained an AUC of 84.36% for predicting anti-VEGF response, surpassing existing methods.
  • Demonstrated strong interpretability and robustness through multimodal integration.

Conclusions:

  • The DME-RWKV model offers a promising AI framework for precise and explainable prediction of anti-VEGF treatment outcomes in DME.
  • The model's ability to mimic clinical reasoning enhances its clinical utility.
  • This approach advances personalized medicine for DME management.