Brenner AR, Laoveeravat P, Carey PJ, Joiner D, Mardini SH, Jovani M. Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction. Artif Intell Gastroenterol 2022; 3(3): 88-95 [DOI: 10.35712/aig.v3.i3.88]
Corresponding Author of This Article
Manol Jovani, MD, MSc, Assistant Professor, Attending Doctor, Doctor, Digestive Diseases and Nutrition, University of Kentucky Albert B. Chandler Hospital, 770 Rose St Room MN662, Lexington, KY 40536, United States. manol.jovani@mail.harvard.edu
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Artif Intell Gastroenterol. Jun 28, 2022; 3(3): 88-95 Published online Jun 28, 2022. doi: 10.35712/aig.v3.i3.88
Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction
Aaron R Brenner, Passisd Laoveeravat, Patrick J Carey, Danielle Joiner, Samuel H Mardini, Manol Jovani
Aaron R Brenner, Patrick J Carey, Danielle Joiner, Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
Passisd Laoveeravat, Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
Samuel H Mardini, Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KENTUCKY 40536, United States
Manol Jovani, Digestive Diseases and Nutrition, University of Kentucky Albert B. Chandler Hospital, Lexington, KY 40536, United States
Author contributions: All authors contributed to the paper with regard to conception and design of the study, literature review and analysis, drafting the manuscript and all authors approved the final version of the manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Manol Jovani, MD, MSc, Assistant Professor, Attending Doctor, Doctor, Digestive Diseases and Nutrition, University of Kentucky Albert B. Chandler Hospital, 770 Rose St Room MN662, Lexington, KY 40536, United States. manol.jovani@mail.harvard.edu
Received: March 7, 2022 Peer-review started: March 7, 2022 First decision: April 10, 2022 Revised: April 16, 2022 Accepted: May 5, 2022 Article in press: May 5, 2022 Published online: June 28, 2022
Abstract
While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.
Core Tip: Artificial intelligence (AI) aided by multiple imaging modalities is accurate and effective for diagnosis and characterization of biliary masses. The advancement and incorporation of imaging into artificial intelligence will help to decrease delay in diagnosis of cholangiocarcinoma and potentially decrease mortality. This review examines studies showing that AI can assist in real-time diagnosis of cholangiocarcinoma and predict outcomes of treatment. Current data suggests that AI will soon become an indispensable part of the armamentarium for the management of cholangiocarcinoma and other biliary diseases.
