人工智能在肝移植中的应用

梁智星; 叶林森; 杨扬

doi:10.3969/j.issn.1001-5256.2022.01.005

人工智能在肝移植中的应用

DOI: 10.3969/j.issn.1001-5256.2022.01.005

中山大学附属第三医院肝脏外科暨肝移植中心, 中山大学器官移植研究所, 广东省器官移植研究中心, 广州 510630

基金项目:

国家“十三五”重大科技专项 (2017ZX10203205-006-001);

国家重点研发计划项目 (2017YFA0104304);

国家自然科学基金 (82103448);

国家自然科学基金 (81770648);

国家自然科学基金 (81972286);

广东省基础与应用基础研究基金 (2019A1515110654);

广东省自然科学基金 (2015A030312013);

广东省科技项目 (2017B020209004);

广东省科技项目 (20169013);

广东省科技项目 (2020B1212060019);

广州市科技项目 (201508020262)

利益冲突声明：所有作者均声明不存在利益冲突。

作者贡献声明：梁智星负责撰写论文；叶林森负责修改论文；杨扬负责拟定写作思路，指导撰写文章并最后定稿。

详细信息

通信作者:
杨扬，yysysu@163.com

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出版历程
- 收稿日期: 2021-10-11
- 录用日期: 2021-10-11
- 出版日期: 2022-01-20

Application of artificial intelligence in liver transplantation

Department of Hepatic Surgery and Liver Transplantation Center of The Third Affiliated Hospital of Sun Yat-Sen University, Organ Transplantation Institute of Sun Yat-sen University, Organ Transplantation Research Center of Guangdong Province, Guangzhou 510630, China

Research funding:

The Major State Research Development Program during the 13rd Five-Year Plan Period (2017ZX10203205-006-001);

National Key R & D Program of China (2017YFA0104304);

National Natural Science Foundation of China (82103448);

National Natural Science Foundation of China (81770648);

National Natural Science Foundation of China (81972286);

Guangdong Basic and Applied Basic Research Foundation (2019A1515110654);

Natural Science Foundation of Guangdong Province (2015A030312013);

Science and Technology Program of Guangdong Province (2017B020209004);

Science and Technology Program of Guangdong Province (20169013);

Science and Technology Program of Guangdong Province (2020B1212060019);

Science and Technology Program of Guangzhou (201508020262)

摘要

摘要: 随着5G和大数据时代的来临, 多维度、大样本的复杂医疗数据为新时代临床医学带来众多机遇和挑战。与传统方法相比, 人工智能可以更高效地挖掘庞大数据集中的隐藏联系, 越来越多的学者将这一先进技术应用于疾病的诊断和治疗。经过半个多世纪的发展和完善, 肝移植已成为治疗各种终末期肝病最有效的方法。与其他领域的“单患者”数据分析不同, 肝移植通常需要考虑供体和受体“双患者”的特征以及移植过程中的变量, 产生较其他疾病更为庞大的医疗数据, 这与人工智能的优势尤为契合。有效利用人工智能的手段、结合临床研究开展将为智能精准临床医学时代拉开新的序幕。对于人工智能在肝移植的交叉应用, 需要全面讨论人工智能技术在肝移植中的优势和局限性, 并提出该领域的未来方向。
- 肝移植 /
- 人工智能 /
- 机器学习 /
- 深度学习
Abstract: With the advent of the era of 5G and big data, complex medical data with multiple dimensions and a large sample size bring both opportunities and challenges for clinical medicine in the new era. Compared with conventional methods, artificial intelligence can detect the hidden patterns within large datasets, and more and more scholars are applying such advanced technology in the diagnosis and treatment of diseases. After development and perfection for more than half a century, liver transplantation has become the most effective treatment method for end-stage liver diseases. Unlike the analysis of "single-patient" data in other fields, liver transplantation usually requires the consideration of the features of both the donor and the recipient and the variables during transplantation, thus generating a larger volume of medical data than other diseases, which is particularly in line with the advantages of artificial intelligence. Effective application of artificial intelligence and its combination with clinical research will usher in the new era of precision medicine. The advantages and limitations of artificial intelligence technology should be comprehensively discussed for the cross-application of artificial intelligence in liver transplantation, and the future directions of this field should also be proposed.
- Liver Transplantation /
- Artificial Intelligence /
- Machine Learning /
- Deep Learning

HTML全文

表 1 常用的AI算法

名称	核心原理	优点	缺点	应用
传统ML监督算法
逻辑回归	使用logistic函数对分类因变量建模	实现简单；计算量小, 对算力要求不高；易于解释各特征比重	容易欠拟合, 准确度欠佳, 不能很好地处理大量多类特征或变量	临床事件预测, 疾病分类, 以往最常用的生物统计学方法
朴素贝叶斯	基于贝叶斯定理的分类器, 即先验概率+数据=后验概率	适用于小规模数据；对缺失数据不敏感	假设变量间相互独立(往往不成立), 需要知道先验概率	临床事件预测疾病分类
支持向量机	利用核函数对高维数据进行非线性分类	适用于高维、非线性数据, 泛化能力好	算力要求高, 可解释性不佳, 对缺失数据敏感	风险预测, 疾病分类, 图像识别
K近邻算法	基于向量间欧氏距离, 将目标以最接近的K个邻近值来代表分类	适用于非线性、多分类, 准确度高	算力要求高, 对数据依赖度高, 对多维数据处理不佳	临床事件预测, 疾病分类
决策树	利用树状模型回答是/否问题以预测可能结果	可解释性强, 无需研究领域知识, 对缺失值不敏感	容易过拟合, 忽略变量间的相关性	临床事件预测, 疾病分类
随机森林	集成多个决策树输出结果, 以最多的结果为最终的输出	适用于高维数据, 准确度较决策树高	容易过拟合, 可解释性一般	临床事件预测, 疾病分类
梯度提升方法	依次迭代多个较弱的预测模型, 结合得到更佳的输出	高准确性, 无需特征筛选	算力要求高, 对离群值敏感	临床事件预测, 疾病分类
神经网络与DL
ANN	多个神经元模型连接构成, 各层神经元间可传递信息	适用于大规模数据集, 挖掘复杂、非线性关系	算力要求很高, “黑箱”模型, 可解释性差	风险预测, 疾病分类
CNN	模拟动物视觉皮层连接模式的深度学习模型	适用于图像类数据集, 自动提取图像空间特征	算力要求很高, 每层输出物理含义不明确	图像识别, 影像诊断
RNN	模拟信号在神经回路中循环传递, 具有“记忆功能”	特别适用于序列数据集	算力要求很高；建模训练难度大	语音/手写体识别临床事件预测
注：ANN, 人工神经网络；CNN, 卷层神经网络；RNN, 循环神经网络。

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