Tian Yingjie, Liu Minghao, Sun Yu, Fu Saiji
School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China.
Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing 100190, China.
ILIVER. 2023 Mar 4;2(1):73-87. doi: 10.1016/j.iliver.2023.02.002. eCollection 2023 Mar.
The liver is the second-largest organ in the human body and is essential for digesting food and removing toxic substances. Viruses, obesity, alcohol use, and other factors can damage the liver and cause liver disease. The diagnosis of liver disease used to depend on the clinical experience of doctors, which made it subjective, difficult, and time-consuming. Deep learning has made breakthroughs in various fields; thus, there is a growing interest in using deep learning methods to solve problems in liver research to assist doctors in diagnosis and treatment. In this paper, we provide an overview of deep learning in liver research using 139 papers from the last 5 years. We also show the relationship between data modalities, liver topics, and applications in liver research using Sankey diagrams and summarize the deep learning methods used for each liver topic, in addition to the relations and trends between these methods. Finally, we discuss the challenges of and expectations for deep learning in liver research.
肝脏是人体第二大器官，对消化食物和清除有毒物质至关重要。病毒、肥胖、饮酒及其他因素会损害肝脏并引发肝病。过去，肝病的诊断依赖医生的临床经验，这使得诊断具有主观性、难度大且耗时。深度学习在各个领域都取得了突破；因此，人们越来越有兴趣使用深度学习方法来解决肝脏研究中的问题，以协助医生进行诊断和治疗。在本文中，我们使用过去5年的139篇论文对肝脏研究中的深度学习进行了概述。我们还使用桑基图展示了数据模态、肝脏主题和肝脏研究应用之间的关系，并总结了用于每个肝脏主题的深度学习方法，以及这些方法之间的关系和趋势。最后，我们讨论了肝脏研究中深度学习面临的挑战和期望。
