A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms

作者：Bo Qian, Bin Sheng, Hao Chen, Xiangning Wang, Tingyao Li, Yixiao Jin, Zhouyu Guan, Zehua Jiang, Yi-Lan Wu, Jinyuan Wang, Ting‐Li Chen, Zhengrui Guo, X. Chen, Dawei Yang, Junlin Hou, Rui Feng, Fan Xiao, Yihao Li, Mostafa El Habib Daho, Lu Li, Y. Ding, Di Liu, Bo Yang, Wenhui Zhu, Yalin Wang, Hyeonmin Kim, Hyeonseob Nam, Huayu Li, Wei‐Chi Wu, Qiang Wu, Rongping Dai, Huating Li, Marcus Ang, Daniel Shu Wei Ting, Carol Y. Cheung, Xiaofei Wang, Ching‐Yu Cheng, Gavin Siew Wei Tan, Kyoko Ohno-Matsui, Jost B. Jonas, Yingfeng Zheng, Yih Chung Tham, Tien Yin Wong, Ya Xing Wang · 发表于：JAMA Ophthalmology · 年份：2024 · DOI：10.1001/jamaophthalmol.2024.3707 · 被引用次数：34 · 研究领域：Ophthalmology and Visual Impairment Studies、Retinal Imaging and Analysis、Retinal Diseases and Treatments

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings. Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists. Design, Setting, and Participants: The Myopic Maculopathy Analysis Challenge (MMAC) was an international competition to develop automated solutions for 3 tasks: (1) MM classification, (2) segmentation of MM plus lesions, and (3) spherical equivalent (SE) prediction. Participants were provided 3 subdatasets containing 2306, 294, and 2003 fundus images, respectively, with which to build algorithms. A group of 5 ophthalmologists evaluated the same test sets for tasks 1 and 2 to ascertain performance. Results from model ensembles, which combined outcomes from multiple algorithms submitted by MMAC participants, were compared with each individual submitted algorithm. This study was conducted from March 1, 2023, to March 30, 2024, and data were analyzed from January 15, 2024, to March 30, 2024. Exposure: DL algorithms submitted as part of the MMAC competition or ophthalmologist interpretation. Main Outcomes and Measures: MM classification was evaluated by quadratic-weighted κ (QWK), F1 score, sensitivity, and specificity. MM plus lesions segmentation was evaluated by dice si...