Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition

• URL: http://arxiv.org/abs/2505.05768v1
• Date: Fri, 09 May 2025 04:12:05 GMT
• Title: Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition
• Authors: 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,
• Abstract summary: We organized the 2nd Asia-Pacific Tele-Ophthalmology Society (APTOS) Big Data Competition in 2021.<n>The competition focused on improving predictive accuracy for anti-VEGF therapy responses using ophthalmic OCT images.<n>This paper details the competition's structure, dataset, leading methods, and evaluation metrics.
• Score: 12.617571811884499
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 2,000 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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