biostudies-literatureUnknown74(1)Zeng LThe 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 nonlinear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modelling and landmarking approaches Journal of the Royal Statistical Society. Series C, Applied statistics187-203https://www.ebi.ac.uk/biostudies/studies/S-EPMC11725344biostudies-literaturetdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.PMC11725344Ding YZhang JZeng LChen WfalsetdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.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 nonlinear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modelling and landmarking approaches 2025-01-01T00:00:00Z2025 Jan2026-06-04T08:56:32.539Z2026-05-07T03:12:03.606ZS-EPMC117253443980717510.1093/jrsssc/qlae051