Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

ABSTRACT: Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing organ shortage. While recent technological advances in organ transplantation have increased their success rate, a substantial challenge in increasing the number of DCD donations resides in the uncertainty regarding the timing of cardiac death after terminal extubation, impacting the risk of prolonged ischemic organ injury, and negatively affecting post-transplant outcomes. In this study, we trained and externally validated an ODE-RNN model, which combines recurrent neural network with neural ordinary equations and excels in processing irregularly-sampled time series data. The model is designed to predict time-to-death following terminal extu

SUBMITTER: Sun X 

PROVIDER: S-EPMC12009369 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature