Institute of Liver Studies, King's College Hospital, Denmark Hill, London, UK; Institute of Hepatology, Foundation for Liver Research, Denmark Hill, London, UK; Faculty of Life Sciences & Medicine, King's College London, Strand, London, UK.
College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
Transplant Proc. 2021 Dec;53(10):2939-2944. doi: 10.1016/j.transproceed.2021.09.045. Epub 2021 Nov 2.
Advancements based on artificial intelligence have emerged in all areas of medicine. Many decisions in organ transplantation can now potentially be addressed in a more precise manner with the aid of artificial intelligence.
METHOD/RESULTS: All elements of liver transplantation consist of a set of input variables and a set of output variables. Artificial intelligence identifies relationships between the input variables; that is, how they select the data groups to train patterns and how they can predict the potential outcomes of the output variables. The most widely used classifiers to address the different aspects of liver transplantation are artificial neural networks, decision tree classifiers, random forest, and naïve Bayes classification models. Artificial intelligence applications are being evaluated in liver transplantation, especially in organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology.
In the years to come, deep learning-based models will be used by liver transplant experts to support their decisions, especially in areas where securing equitability in the transplant process needs to be optimized.
人工智能在医学的各个领域都取得了进展。现在，许多器官移植决策都可以借助人工智能更精确地解决。
方法/结果：肝移植的所有要素都由一组输入变量和一组输出变量组成。人工智能识别输入变量之间的关系，即它们如何选择数据组来训练模式，以及它们如何预测输出变量的潜在结果。用于解决肝移植不同方面的最广泛使用的分类器是人工神经网络、决策树分类器、随机森林和朴素贝叶斯分类模型。人工智能应用正在肝移植中进行评估，特别是在器官分配、供体-受者匹配、生存预测分析和移植肿瘤学方面。
在未来几年，基于深度学习的模型将被肝移植专家用来支持他们的决策，特别是在需要优化移植过程公平性的领域。
