Investigative Medicine Program, Yale University School of Medicine, New Haven, Connecticut, USA.
Oxford Artificial Intelligence Programme, University of Oxford, Oxford, UK.
J Gastroenterol Hepatol. 2023 Jun;38(6):874-882. doi: 10.1111/jgh.16180. Epub 2023 Mar 28.
Artificial intelligence (AI), by means of computer vision in machine learning, is a promising tool for cholangiocarcinoma (CCA) diagnosis. The aim of this study was to provide a comprehensive overview of AI in medical imaging for CCA diagnosis.
A systematic review with scientometric analysis was conducted to analyze and visualize the state-of-the-art of medical imaging to diagnosis CCA.
Fifty relevant articles, published by 232 authors and affiliated with 68 organizations and 10 countries, were reviewed in depth. The country with the highest number of publications was China, followed by the United States. Collaboration was noted for 51 (22.0%) of the 232 authors forming five clusters. Deep learning algorithms with convolutional neural networks (CNN) were the most frequently used classifiers. The highest performance metrics were observed with CNN-cholangioscopy for diagnosis of extrahepatic CCA (accuracy 94.9%; sensitivity 94.7%; and specificity 92.1%). However, some of the values for CNN in CT imaging for diagnosis of intrahepatic CCA were low (AUC 0.72 and sensitivity 44%).
Our results suggest that there is increasing evidence to support the role of AI in the diagnosis of CCA. CNN-based computer vision of cholangioscopy images appears to be the most promising modality for extrahepatic CCA diagnosis. Our social network analysis highlighted an Asian and American predominance in the research relational network of AI in CCA diagnosis. This discrepancy presents an opportunity for coordination and increased collaboration, especially with institutions located in high CCA burdened countries.
人工智能（AI）通过机器学习中的计算机视觉，是诊断胆管癌（CCA）的有前途的工具。本研究旨在提供用于诊断 CCA 的医学成像 AI 的全面概述。
进行了系统评价和科学计量分析，以分析和可视化用于诊断 CCA 的医学成像的最新技术。
共深入审查了 50 篇相关文章，这些文章由 232 位作者发表，这些作者隶属于 68 个组织和 10 个国家。发表文章数量最多的国家是中国，其次是美国。有 51 位（22.0%）作者进行了合作，形成了五个聚类。使用卷积神经网络（CNN）的深度学习算法是最常使用的分类器。在胆管镜检查中用于诊断肝外 CCA 的 CNN 具有最高的性能指标（准确度为 94.9%；灵敏度为 94.7%；特异性为 92.1%）。但是，在 CT 成像中用于诊断肝内 CCA 的 CNN 的一些值较低（AUC 为 0.72，灵敏度为 44%）。
我们的研究结果表明，越来越多的证据支持 AI 在 CCA 诊断中的作用。基于 CNN 的胆管镜图像计算机视觉似乎是诊断肝外 CCA 的最有前途的方法。我们的社会网络分析突出了亚洲和美国在 AI 诊断 CCA 的研究关系网络中占据主导地位。这种差异为协调和加强合作提供了机会，特别是与高 CCA 负担国家的机构合作。
