Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories and are unable to predict future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically discover biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. Then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. These are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system.

翻译：年龄相关性黄斑变性（AMD）是导致老年人失明的主要原因。当前基于影像生物标志物的分级系统仅将疾病阶段粗略划分为宽泛类别，且无法预测未来的疾病进展。普遍认为，这是由于这些系统仅关注单一时间点，忽视了疾病的动态特性。在本研究中，我们提出了首个能够自动发现捕捉疾病进展时间动态特征的生物标志物的方法。该方法将患者时间序列表示为通过对比学习构建的潜在特征空间中的轨迹，然后将个体轨迹分割为编码疾病状态间转换的原子子序列，并利用新引入的距离度量对这些子序列进行聚类。定量实验表明，我们的方法所产生的时间生物标志物能够预测AMD晚期转变的风险。此外，这些聚类结果对眼科医生具有高度可解释性，他们证实许多聚类所代表的动态特征此前已被证实与AMD进展相关，但至今尚未被纳入任何临床分级系统。