Unit of Liver Transplantation, Department of General Surgery, Hospital Universitario Reina Sofía, Córdoba, Spain; Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain.
Unit of Liver Transplantation, Department of General Surgery, Hospital Universitario Reina Sofía, Córdoba, Spain; Maimónides Institute of Biomedical Research of Córdoba (IMIBIC), Córdoba, Spain.
Hepatobiliary Pancreat Dis Int. 2022 Aug;21(4):347-353. doi: 10.1016/j.hbpd.2022.03.001. Epub 2022 Mar 8.
Decision-making based on artificial intelligence (AI) methodology is increasingly present in all areas of modern medicine. In recent years, models based on deep-learning have begun to be used in organ transplantation. Taking into account the huge number of factors and variables involved in donor-recipient (D-R) matching, AI models may be well suited to improve organ allocation. AI-based models should provide two solutions: complement decision-making with current metrics based on logistic regression and improve their predictability. Hundreds of classifiers could be used to address this problem. However, not all of them are really useful for D-R pairing. Basically, in the decision to assign a given donor to a candidate in waiting list, a multitude of variables are handled, including donor, recipient, logistic and perioperative variables. Of these last two, some of them can be inferred indirectly from the team's previous experience. Two groups of AI models have been used in the D-R matching: artificial neural networks (ANN) and random forest (RF). The former mimics the functional architecture of neurons, with input layers and output layers. The algorithms can be uni- or multi-objective. In general, ANNs can be used with large databases, where their generalizability is improved. However, they are models that are very sensitive to the quality of the databases and, in essence, they are black-box models in which all variables are important. Unfortunately, these models do not allow to know safely the weight of each variable. On the other hand, RF builds decision trees and works well with small cohorts. In addition, they can select top variables as with logistic regression. However, they are not useful with large databases, due to the extreme number of decision trees that they would generate, making them impractical. Both ANN and RF allow a successful donor allocation in over 80% of D-R pairing, a number much higher than that obtained with the best statistical metrics such as model for end-stage liver disease, balance of risk score, and survival outcomes following liver transplantation scores. Many barriers need to be overcome before these deep-learning-based models can be included for D-R matching. The main one of them is the resistance of the clinicians to leave their own decision to autonomous computational models.
基于人工智能（AI）方法的决策在现代医学的各个领域越来越普遍。近年来，基于深度学习的模型开始应用于器官移植。考虑到供体-受体（D-R）匹配中涉及的大量因素和变量，AI 模型可能非常适合改善器官分配。基于 AI 的模型应该提供两种解决方案：用基于逻辑回归的当前指标补充决策，并提高其可预测性。可以使用数百个分类器来解决这个问题。然而，并非所有分类器都真正有助于 D-R 配对。基本上，在决定将给定的供体分配给候补名单上的候选人时，会处理许多变量，包括供体、受体、逻辑和围手术期变量。在最后两类变量中，有些可以间接从团队的以往经验中推断出来。在 D-R 匹配中使用了两类 AI 模型：人工神经网络（ANN）和随机森林（RF）。前者模拟神经元的功能架构，具有输入层和输出层。算法可以是单目标或多目标。一般来说，ANN 可以与大型数据库一起使用，从而提高其通用性。但是，它们是对数据库质量非常敏感的模型，本质上是所有变量都很重要的黑盒模型。不幸的是，这些模型不能安全地知道每个变量的权重。另一方面，RF 构建决策树，在小队列中效果很好。此外，它们可以像逻辑回归一样选择顶级变量。但是，它们对于大型数据库没有用，因为它们会生成数量极多的决策树，使其不切实际。ANN 和 RF 都允许在超过 80%的 D-R 配对中成功分配供体，这一数字远高于模型终末期肝病、风险评分平衡和肝移植后生存率等最佳统计指标所获得的数字。在这些基于深度学习的模型可以用于 D-R 匹配之前，需要克服许多障碍。其中主要的障碍是临床医生对将自己的决策留给自主计算模型的抵制。
