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

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 framework to predict the outcome after liver transplantation. An individually designed neural network was developed to meet the unique requirements of transplantation data. The metrics used to determine the model quality and its level of performance are accuracy, cross-entropy loss, and F1 score as well as AUC score.

Results: A total of 529 transplantations with a total of 1058 matching donor and recipient observations were added into the database. The combined prediction of all outcome parameters was 95.8% accurate (cross-entropy loss of 0.042). The prediction of death within the hospital was 94.3% accurate (cross-entropy loss of 0.057). The overall F1 score was 0.899 on average, whereas the overall AUC score was 0.940.

Conclusion: With the achieved results, the network serves as a reliable tool to predict survival. It adds new insight into the potential of deep learning to assist medical decisions. Especially in the field of transplantation, an AUC Score of 94% is very valuable. This neuronal network is unique as it utilizes transparent and easily interpretable data to predict the outcome after liver transplantation. Further validation must be performed prior to utilization in a clinical context.