标题： 深度学习在视网膜退行性疾病评估中的应用：MARIO 年龄相关性黄斑变性进展挑战的综合分析 显示英文标题

标题： Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO AMD Progression Challenge

摘要： MARIO挑战赛于2024年MICCAI会议上举行，专注于通过光学相干断层扫描（OCT）图像分析来推动年龄相关性黄斑变性（AMD）的自动化检测和监测。 该挑战旨在评估算法在检测AMD中新生血管活动变化方面的性能，并纳入了独特的多模态数据集。 主要数据集来自法国布雷斯特，参赛团队使用此数据集训练和测试他们的模型。 最终排名基于在此数据集上的表现。 阿尔及利亚的辅助数据集在挑战赛后用于评估提交解决方案的种群和设备偏移。 MARIO挑战赛涉及两项任务。 第一项任务是连续两个二维OCT B扫描之间的演变分类。 第二项任务是在抗血管内皮生长因子（VEGF）治疗的患者中预测未来三个月的AMD演变。 共有35支队伍参加，其中前12名决赛队伍展示了他们的方法。 本文概述了挑战赛的结构、任务、数据特征和获胜方法，为使用OCT、红外成像和临床数据（如就诊次数、年龄、性别等）监测AMD设定了基准。 该挑战的结果表明，人工智能（AI）在测量AMD进展（任务1）方面表现与医生相当，但尚不能预测未来的演变（任务2）。 显示英文摘要

摘要： The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical coherence tomography (OCT) images. Designed to evaluate algorithmic performance in detecting neovascular activity changes within AMD, the challenge incorporated unique multi-modal datasets. The primary dataset, sourced from Brest, France, was used by participating teams to train and test their models. The final ranking was determined based on performance on this dataset. An auxiliary dataset from Algeria was used post-challenge to evaluate population and device shifts from submitted solutions. Two tasks were involved in the MARIO challenge. The first one was the classification of evolution between two consecutive 2D OCT B-scans. The second one was the prediction of future AMD evolution over three months for patients undergoing anti-vascular endothelial growth factor (VEGF) therapy. Thirty-five teams participated, with the top 12 finalists presenting their methods. This paper outlines the challenge's structure, tasks, data characteristics, and winning methodologies, setting a benchmark for AMD monitoring using OCT, infrared imaging, and clinical data (such as the number of visits, age, gender, etc.). The results of this challenge indicate that artificial intelligence (AI) performs as well as a physician in measuring AMD progression (Task 1) but is not yet able of predicting future evolution (Task 2).