A Novel Deep Learning Model as a Donor-Recipient Matching Tool to Predict Survival after Liver Transplantation.

ABSTRACT:

Background

The "digital era" in the field of medicine is the new "here and now". Artificial intelligence has entered many fields of medicine and is recently emerging in the field of organ transplantation. Solid organs remain a scarce resource. Being able to predict the outcome after liver transplantation promises to solve one of the long-standing problems within organ transplantation. What is the perfect donor recipient match? Within this work we developed and validated a novel deep-learning-based donor-recipient allocation system for liver transplantation.

Method

In this study we used data collected from all liver transplant patients between 2004 and 2019 at the university transplantation centre in Munich. We aimed to design a transparent and interpretable deep learning fra

SUBMITTER: Borner N 

PROVIDER: S-EPMC9655123 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature