Gulla Aiste, Jakiunaite Ieva, Juchneviciute Ivona, Dzemyda Gintautas
Faculty of Medicine, Institute of Clinical Medicine, Vilnius University, Vilnius, Lithuania.
Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Front Transplant. 2024 May 13;3:1378378. doi: 10.3389/frtra.2024.1378378. eCollection 2024.
Liver transplantation is the only treatment for patients with liver failure. As demand for liver transplantation grows, it remains a challenge to predict the short- and long-term survival of the liver graft. Recently, artificial intelligence models have been used to evaluate the short- and long-term survival of the liver transplant. To make the models more accurate, suitable liver transplantation characteristics must be used as input to train them. In this narrative review, we reviewed studies concerning liver transplantations published in the PubMed, Web of Science, and Cochrane databases between 2017 and 2022. We picked out 17 studies using our selection criteria and analyzed them, evaluating which medical characteristics were used as input for creation of artificial intelligence models. In eight studies, models estimating only short-term liver graft survival were created, while in five of the studies, models for the prediction of only long-term liver graft survival were built. In four of the studies, artificial intelligence algorithms evaluating both the short- and long-term liver graft survival were created. Medical characteristics that were used as input in reviewed studies and had the biggest impact on the accuracy of the model were the recipient's age, recipient's body mass index, creatinine levels in the recipient's serum, recipient's international normalized ratio, diabetes mellitus, and recipient's model of end-stage liver disease score. To conclude, in order to define important liver transplantation characteristics that could be used as an input for artificial intelligence algorithms when predicting liver graft survival, more models need to be created and analyzed, in order to fully support the results of this review.
肝移植是肝功能衰竭患者的唯一治疗方法。随着肝移植需求的增加，预测肝移植短期和长期存活情况仍然是一项挑战。最近，人工智能模型已被用于评估肝移植的短期和长期存活情况。为了使模型更准确，必须使用合适的肝移植特征作为输入来训练它们。在这篇叙述性综述中，我们回顾了2017年至2022年间发表在PubMed、科学网和Cochrane数据库中有关肝移植的研究。我们根据选择标准挑选出17项研究并进行分析，评估哪些医学特征被用作创建人工智能模型的输入。在8项研究中，创建了仅估计肝移植短期存活的模型，而在5项研究中，构建了仅预测肝移植长期存活的模型。在4项研究中，创建了评估肝移植短期和长期存活的人工智能算法。在综述研究中用作输入且对模型准确性影响最大的医学特征包括受者年龄、受者体重指数、受者血清肌酐水平、受者国际标准化比值、糖尿病以及受者终末期肝病模型评分。总之，为了确定在预测肝移植存活时可作为人工智能算法输入的重要肝移植特征，需要创建和分析更多模型，以便充分支持本综述的结果。
