Applications of neural networks in liver transplantation

The use of neural networks (NNs) as a cutting-edge technique in the medical field has drawn considerable attention. NN models "learn" from a large amount of data and then find corresponding clinical patterns that are challenging for clinicians to recognize. In this study, we focus on liver transplantation (LT), which is an effective treatment for end-stage liver diseases. The management before and after LT produces a massive quantity of medical data, which can be fully processed by NNs. We describe recent progress in the clinical application of NNs to LT in five respects: pre-transplantation evaluation of the donor and recipient, recipient outcome prediction, allocation system development, operation monitoring, and post-transplantation complication prediction. This review provides clinicians and researchers with a description of forefront applications of NNs in the field of LT and discusses prospects and pitfalls.