Xiong Ming, Xu Yaona, Zhao Yang, He Si, Zhu Qihan, Wu Yi, Hu Xiaofei, Liu Li
Department of Digital Medicine, School of Biomedical Engineering and Medical Imaging, Third Military Medical University (Army Medical University), Chongqing, China.
Department of Nuclear Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
Front Oncol. 2023 Feb 16;13:990306. doi: 10.3389/fonc.2023.990306. eCollection 2023.
To provide the current research progress, hotspots, and emerging trends for AI in liver cancer, we have compiled a relative comprehensive and quantitative report on the research of liver disease using artificial intelligence by employing bibliometrics in this study.
In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords and a manual screening strategy, VOSviewer was used to analyze the degree of cooperation between countries/regions and institutions, as well as the co-occurrence of cooperation between authors and cited authors. Citespace was applied to generate a dual map to analyze the relationship of citing journals and citied journals and conduct a strong citation bursts ranking analysis of references. Online SRplot was used for in-depth keyword analysis and Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles.
1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were also common. Computed tomography was the most used diagnostic tool, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer.
AI has undergone rapid development and has a wide application in the diagnosis and treatment of liver diseases, especially in China. Imaging is an indispensable tool in this filed. Mmulti-type data fusion analysis and development of multimodal treatment plans for liver cancer could become the major trend of future research in AI in liver cancer.
为了提供人工智能在肝癌研究方面的当前研究进展、热点和新趋势，我们在本研究中采用文献计量学方法，编写了一份关于利用人工智能进行肝脏疾病研究的相对全面且定量的报告。
在本研究中，使用科学网核心合集（WoSCC）数据库，通过关键词和手动筛选策略进行系统检索，使用VOSviewer分析国家/地区与机构之间的合作程度，以及作者与被引作者之间的合作共现情况。应用Citespace生成双图，以分析引用期刊与被引期刊的关系，并对参考文献进行强引用爆发排名分析。使用在线SRplot进行深入的关键词分析，并使用Microsoft Excel 2019从检索到的文章中收集目标变量。
本研究共收集到1724篇论文，其中包括1547篇原创文章和177篇综述。人工智能在肝癌方面的研究大多始于2003年，自2017年以来发展迅速。中国的出版物数量最多，美国的H指数和总被引次数最高。产出最多的前三个机构是欧洲研究型大学联盟、中山大学和浙江大学。Jasjit S. Suri分别是发文最多的作者和期刊。关键词分析表明，除了肝癌研究外，肝硬化、脂肪肝疾病和肝纤维化的研究也很常见。计算机断层扫描是最常用的诊断工具，其次是超声和磁共振成像。目前，肝癌的诊断和鉴别诊断是最广泛采用的研究目标，对多类型数据的综合分析和晚期肝癌患者的术后分析较少。卷积神经网络的使用是人工智能在肝癌研究中使用的主要技术方法。
人工智能发展迅速，在肝脏疾病的诊断和治疗中有着广泛应用，尤其是在中国。影像学是该领域不可或缺的工具。多类型数据融合分析以及肝癌多模态治疗方案的制定可能成为未来人工智能在肝癌研究中的主要趋势。
