Meng Jinwen, Liu Zhikun, Xu Xiao
Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024, China.
ILIVER. 2022 Aug 9;1(2):101-110. doi: 10.1016/j.iliver.2022.07.002. eCollection 2022 Jun.
The use of neural networks (NNs) as a cutting-edge technique in the medical field has drawn considerable attention. NN models "learn" from a large amount of data and then find corresponding clinical patterns that are challenging for clinicians to recognize. In this study, we focus on liver transplantation (LT), which is an effective treatment for end-stage liver diseases. The management before and after LT produces a massive quantity of medical data, which can be fully processed by NNs. We describe recent progress in the clinical application of NNs to LT in five respects: pre-transplantation evaluation of the donor and recipient, recipient outcome prediction, allocation system development, operation monitoring, and post-transplantation complication prediction. This review provides clinicians and researchers with a description of forefront applications of NNs in the field of LT and discusses prospects and pitfalls.
神经网络（NNs）作为医学领域的一项前沿技术，已引起了广泛关注。NN模型从大量数据中“学习”，然后找出临床医生难以识别的相应临床模式。在本研究中，我们聚焦于肝移植（LT），这是治疗终末期肝病的有效方法。LT前后的管理产生了大量医疗数据，这些数据可由NNs进行全面处理。我们从五个方面描述了NNs在LT临床应用中的最新进展：供体和受体的移植前评估、受体结局预测、分配系统开发、手术监测以及移植后并发症预测。本综述向临床医生和研究人员介绍了NNs在LT领域的前沿应用，并探讨了前景与陷阱。
