The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the non-linear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network (CNN) with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is a large AMD study, the Age-Related Eye Disease Study (AREDS), in which more than 50,000 fundus images were captured over a period of 12 years for more than 4,000 participants. Another is a public dataset of the primary biliary cirrhosis (PBC) disease, where multiple lab tests were longitudinally collected to predict the time-to-liver transplant. Our approach demonstrates commendable predictive performance in both simulation studies and the analysis of the two real datasets.

翻译：动态预测的目标是随时间提供个体化风险预测，并在新数据可用时进行更新。为构建针对渐进性眼病——年龄相关性黄斑变性（AMD）的动态预测模型，我们提出时变Cox生存神经网络（tdCoxSNN），利用纵向眼底图像预测其进展。tdCoxSNN基于时变Cox模型，通过神经网络捕捉时变协变量对生存结局的非线性影响。此外，通过将卷积神经网络（CNN）与生存网络并行集成，tdCoxSNN可直接将纵向图像作为输入。我们通过大量模拟研究评估了所提方法，并与联合建模和界标法进行比较。我们将该方法应用于两个真实数据集：其一是大型AMD研究——年龄相关性眼病研究（AREDS），该研究在12年间对4000多名参与者采集了超过5万张眼底图像；另一个是原发性胆汁性胆管炎（PBC）疾病的公开数据集，其中纵向收集了多项实验室检测结果以预测肝移植时间。在模拟研究及两个真实数据集分析中，我们的方法均展现出优异的预测性能。