用OCT影像预测糖尿病黄斑水肿治疗反应，助力个性化诊疗
Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition
- 基于OCT图像构建2000名患者数据集，设计四类预测任务
- 顶尖模型在抗VEGF治疗响应预测上达到80.06% AUC
- 为眼科AI辅助决策提供高质量数据与方法参考
糖尿病性黄斑水肿（DME）是导致糖尿病患者视力下降的主要原因，而抗血管内皮生长因子（anti-VEGF）治疗的疗效存在显著个体差异，亟需患者分层以预测治疗获益并实现个性化干预。本研究首次探索了治疗前分层预测DME治疗反应的方法。为推动该领域发展，我们于2021年组织第二届亚太远程眼科协会（APTOS）大数据竞赛，聚焦利用眼底OCT图像提升anti-VEGF治疗响应预测精度。竞赛提供包含两万名患者、数十万张OCT图像的数据集，涵盖四个子任务标签。本文详述竞赛结构、数据集构成、领先方法及评估指标。共有170支团队注册，41支进入决赛。冠军团队在测试集上取得80.06%的AUC，验证了人工智能在个性化DME治疗和临床决策中的潜力。
原文摘要 · Abstract (English)
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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