Neovascular age-related macular degeneration (nAMD) is a leading cause of vision loss among older adults, where disease activity detection and progression prediction are critical for nAMD management in terms of timely drug administration and improving patient outcomes. Recent advancements in deep learning offer a promising solution for predicting changes in AMD from optical coherence tomography (OCT) retinal volumes. In this work, we proposed deep learning models for the two tasks of the public MARIO Challenge at MICCAI 2024, designed to detect and forecast changes in nAMD severity with longitudinal retinal OCT. For the first task, we employ a Vision Transformer (ViT) based Siamese Network to detect changes in AMD severity by comparing scan embeddings of a patient from different time points. To train a model to forecast the change after 3 months, we exploit, for the first time, an Earth Mover (Wasserstein) Distance-based loss to harness the ordinal relation within the severity change classes. Both models ranked high on the preliminary leaderboard, demonstrating that their predictive capabilities could facilitate nAMD treatment management.

翻译：新生血管性年龄相关性黄斑变性（nAMD）是导致老年人视力丧失的主要原因，其疾病活动性检测与进展预测对于通过及时给药和改善患者预后进行nAMD管理至关重要。深度学习的最新进展为通过光学相干断层扫描（OCT）视网膜容积预测AMD变化提供了前景广阔的解决方案。本研究针对MICCAI 2024公开MARIO挑战赛的两项任务提出了深度学习模型，旨在利用纵向视网膜OCT检测并预测nAMD严重程度的变化。针对第一项任务，我们采用基于Vision Transformer（ViT）的孪生网络，通过比较患者不同时间点的扫描嵌入向量来检测AMD严重程度变化。为训练预测三个月后变化的模型，我们首次利用基于Earth Mover（Wasserstein）距离的损失函数，以利用严重程度变化类别间的序数关系。两个模型在初步排行榜中均名列前茅，证明其预测能力可促进nAMD治疗管理。