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

Le résumé fourni par la source

• First challenge to focus on pre-treatment stratification for diabetic macular edema (DME): This study pioneers the use of pre-treatment OCT biomarkers to predict individual responses to anti-VEGF therapy, advancing the concept of personalized medicine in DME management. • Large-scale, publicly accessible OCT dataset: The competition provides one of the most comprehensive open-access DME datasets to date, comprising tens of thousands of OCT images from 2,000 patients, significantly addressing the field’s data scarcity. The dataset includes both per-eye and per-scan annotations for several critical retinal biomarkers, enabling a wide range of supervised learning applications. • Benchmark for future research: With 170 registered teams and 41 finalists, the challenge fostered broad engagement. The best team achieved an AUC of 80.06%, highlighting the feasibility of accurate outcome prediction. The challenge provides standardized evaluation metrics and a curated leaderboard, laying the groundwork for reproducible and comparable AI model development in ophthalmic treatment prediction. 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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