Predicting diabetic macular edema treatment responses using OCT: Dataset and methods of APTOS competition

IF 14 1区医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Medical image analysis Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI:10.1016/j.media.2026.103942

Weiyi Zhang , Peranut Chotcomwongse , Yinwen Li , Pusheng Xu , Ruijie Yao , Lianhao Zhou , Yuxuan Zhou , Hui Feng , Qiping Zhou , Xinyue Wang , Shoujin Huang , Zihao Jin , Florence H T Chung , Shujun Wang , Yalin Zheng , Mingguang He , Danli Shi , Paisan Ruamviboonsuk

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引用次数: 0

Abstract

Diabetic macular edema (DME) significantly contributes to visual impairment in diabetic patients. Treatment responses to intravitreal therapies vary, highlighting the need for patient stratification to predict therapeutic benefits and enable personalized strategies. To our knowledge, this study is the first to explore pre-treatment stratification for predicting DME treatment responses. To advance this research, we organized the 2nd Asia-Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition in 2021. The competition focused on improving predictive accuracy for anti-VEGF therapy responses using ophthalmic OCT images. We provided a dataset containing tens of thousands of OCT images from 2000 patients with labels across four sub-tasks. This paper details the competition’s structure, dataset, leading methods, and evaluation metrics. The competition attracted strong scientific community participation, with 170 teams initially registering and 41 reaching the final round. The top-performing team achieved an AUC of 80.06 %, highlighting the potential of AI in personalized DME treatment and clinical decision-making.

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使用OCT预测糖尿病黄斑水肿治疗反应：APTOS竞争的数据集和方法

糖尿病性黄斑水肿（DME）对糖尿病患者的视力损害有重要影响。玻璃体内治疗的治疗反应各不相同，因此需要对患者进行分层，以预测治疗效果并实现个性化策略。据我们所知，这项研究是第一个探索治疗前分层预测二甲醚治疗反应的研究。为了推进这一研究，我们于2021年组织了第二届亚太远程眼科学会（APTOS）大数据竞赛。比赛的重点是提高使用眼科OCT图像预测抗vegf治疗反应的准确性。我们提供了一个包含来自2000名患者的数万张OCT图像的数据集，这些图像的标签跨越四个子任务。本文详细介绍了竞赛的结构、数据集、领先方法和评估指标。该竞赛吸引了科学界的强烈参与，最初有170个团队注册，41个团队进入了决赛。表现最好的团队的AUC达到80.06%，突出了人工智能在个性化DME治疗和临床决策方面的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical image analysis 工程技术-工程：生物医学

CiteScore

22.10

自引率

6.40%

发文量

309

审稿时长

6.6 months

期刊介绍： Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.