Crossroads in Liver Transplantation: Is Artificial Intelligence the Key to Donor-Recipient Matching?

Abstract

Liver transplantation outcomes have improved in recent years. However, with the emergence of expanded donor criteria, tools to better assist donor-recipient matching have become necessary. Most of the currently proposed scores based on conventional biostatistics are not good classifiers of a problem that is considered "unbalanced." In recent years, the implementation of artificial intelligence in medicine has experienced exponential growth. Deep learning, a branch of artificial intelligence, may be the answer to this classification problem. The ability to handle a large number of variables with speed, objectivity, and multi-objective analysis is one of its advantages. Artificial neural networks and random forests have been the most widely used deep classifiers in this field. This review aims to give a brief overview of D-R matching and its evolution in recent years and how artificial intelligence may be able to provide a solution.

Keywords: artificial intelligence; artificial neural networks; deep learning; donor–recipient matching; liver transplantation outcomes; random forest.

Figure 1

Different D–R matching systems based on donor and recipient variables. COD, cause of death; CVA, cardiovascular accident; DCDD, donation after circulatory determination of death; PVT, portal vein thrombosis. Figure obtained from Briceno J, Ciria R, de la Mata M. Donor–recipient matching: myths and realities. J. Hepatol. 2013, 58 (4), 811–820. Copyright © 2022 European Association for the Study of the Liver. Published by Elsevier Ireland Ltd. All rights reserved.

Figure 2

Representation of a basic neural network. Different layers are represented in blue (input layer), gray (hidden layer), and yellow (output layer). The arrows represent the relationships between neurons (weights).

Figure 3

Diagram of an ANN-based on a multi-objective algorithm. Liver transplantation outcomes are shown as an unbalanced problem, and we classify them into the majority class (probability of surviving after liver transplantation, NN-CCR) and the minority class (probability of not surviving, NN-MS). By combining both probabilities (NN-CCR and NN-MS) based on input variables, we obtain a final D–R matching according to a rules-based system. ANN, artificial neural network; NN-CCR, neural network based on the correct classification rate or accuracy; NN-MS, neural network based on the minimum sensitivity.

Figure 4

3-month graft survival model based on ANN compared to other scores using the KCH database. CCR (correct classification rate or accuracy), MS (minimum sensitivity), and AUC (area under curve) values are shown. Figure obtained from Ayllón MD et al. [21] © 2022 by the American Association for the Study of Liver Diseases.