Artificial intelligence in medical imaging for cholangiocarcinoma diagnosis: A systematic review with scientometric analysis

Abstract

Introduction: 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.

Methods: A systematic review with scientometric analysis was conducted to analyze and visualize the state-of-the-art of medical imaging to diagnosis CCA.

Results: 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%).

Conclusion: 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.

Keywords: Artificial intelligence; Cholangiocarcinoma; Imaging; Scientometrics; Systematic review.