Liver Transplantation Unit, General and Digestive Surgery Department, Reina Sofía University Hospital, 14004 Cordoba, Spain.
GC18 Translational Research in Solid Organ Transplantation Surgery-Maimonides Biomedical Research Institute (IMIBIC), 14004 Cordoba, Spain.
Medicina (Kaunas). 2022 Nov 28;58(12):1743. doi: 10.3390/medicina58121743.
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.
近年来，肝移植的效果有所改善。然而，随着供体标准的扩大，需要有更好的工具来辅助供体与受者的匹配。目前大多数基于传统生物统计学的评分方法都不能很好地对这个被认为是“不平衡”的问题进行分类。近年来，人工智能在医学领域的应用呈指数级增长。深度学习作为人工智能的一个分支，可能是解决这个分类问题的答案。它能够快速、客观、多目标地处理大量变量，这是其优势之一。人工神经网络和随机森林是该领域应用最广泛的深度学习分类器。本文旨在简要概述 D-R 匹配及其近年来的发展，并探讨人工智能如何提供解决方案。
