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iLIVER 2022, 1(2): 101-110 https://doi.org/10.1016/j.iliver.2022.07.002
Review | Open Access | Issue | Published: 09 August 2022

Applications of neural networks in liver transplantation

Show Author's Information Jinwen Menga,  Zhikun Liua,  Xiao Xua,b,c,d ( )
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
NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, 310003, China
Institute of Organ Transplantation, Zhejiang University, Hangzhou, 310003, China
Keywords:
Artificial intelligence, Deep learning, Machine learning, Neural network, Liver transplantation
Cite this article:
Meng J, Liu Z, Xu X. Applications of neural networks in liver transplantation. iLIVER, 2022, 1(2): 101-110. https://doi.org/10.1016/j.iliver.2022.07.002
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Abstract Full text About this article

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.


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About this article

Applications of neural networks in liver transplantation

Show Author's information Jinwen Menga, Zhikun Liua, Xiao Xua,b,c,d ( )
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
NHC Key Laboratory of Combined Multi-organ Transplantation, Hangzhou, 310003, China
Institute of Organ Transplantation, Zhejiang University, Hangzhou, 310003, China

Abstract

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.

Keywords: Artificial intelligence, Deep learning, Machine learning, Neural network, Liver transplantation

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Received: 06 April 2022
Revised: 07 July 2022
Accepted: 15 July 2022
Published: 09 August 2022
Issue date: June 2022

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© 2022 Published by Elsevier Ltd on behalf of Tsinghua University Press.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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