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Centre-involved Diabetic Macular Edema (ci-DME) is a major cause of vision impairment and can arise at any stage of diabetic retinopathy (DR), with increased prevalence as DR progresses. Early detection is critical for preventing vision loss. Recent advancements in imaging techniques, such as ultra-wide-field color fundus photography (UWF-CFP) and optical coherence tomography (OCT), coupled with the power of deep learning (DL), now enable more precise detection, classification, and grading of DME. The Device-Independent Diabetic Macular Edema Onset Prediction (DIAMOND) Challenge aims to develop DL models capable of predicting whether a patient will develop center-involved diabetic macular edema (ci-DME) within a year using UWF-CFP images, while preventing participants from direct data access to ensure model generalizability in real-world settings. For this challenge, we implemented advanced preprocessing techniques to mitigate out-of-domain data issues and explored multiple DL architectures. Our methodology has shown great promise, positioning our team within the Top 3 of the competition. However, due to the affiliation of some team members with the challenge organizers, we are ineligible for the prize.

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Automatic Prediction of Center-Involved Diabetic Macular Edema Using Ultra-Widefield Color Fundus Photography

  • 会议论文

  • 发表日期：

    2025年

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  • Philippe Zhang,
  • Yihao Li,
  • Weili Jiang,
  • Jing Zhang,
  • Sarah Matta,
  • Yubo Tan,
  • Hui Lin,
  • Haoshen Wang,
  • Jiangtian Pan,
  • Hui Xu,
  • Laurent Borderie,
  • Alexandre Le Guilcher,
  • Béatrice Cochener,
  • Chubin Ou,
  • Gwenolé Quellec,
  • Mathieu Lamard

摘要

Centre-involved Diabetic Macular Edema (ci-DME) is a major cause of vision impairment and can arise at any stage of diabetic retinopathy (DR), with increased prevalence as DR progresses. Early detection is critical for preventing vision loss. Recent advancements in imaging techniques, such as ultra-wide-field color fundus photography (UWF-CFP) and optical coherence tomography (OCT), coupled with the power of deep learning (DL), now enable more precise detection, classification, and grading of DME. The Device-Independent Diabetic Macular Edema Onset Prediction (DIAMOND) Challenge aims to develop DL models capable of predicting whether a patient will develop center-involved diabetic macular edema (ci-DME) within a year using UWF-CFP images, while preventing participants from direct data access to ensure model generalizability in real-world settings. For this challenge, we implemented advanced preprocessing techniques to mitigate out-of-domain data issues and explored multiple DL architectures. Our methodology has shown great promise, positioning our team within the Top 3 of the competition. However, due to the affiliation of some team members with the challenge organizers, we are ineligible for the prize.