Laoveeravat P, Abhyankar PR, Brenner AR, Gabr MM, Habr FG, Atsawarungruangkit A. Artificial intelligence for pancreatic cancer detection: Recent development and future direction . Artif Intell Gastroenterol 2021; 2(2): 56-68 [DOI: 10.35712/aig.v2.i2.56]
Corresponding Author of This Article
Amporn Atsawarungruangkit, MD, Academic Fellow, Instructor, Research Fellow, Division of Gastroenterology, Warren Alpert Medical School of Brown University, 593 Eddy Street, Providence, RI 02903, United States. amporn_atsawarungruangkit@brown.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/
Artificial intelligence for pancreatic cancer detection: Recent development and future direction
Passisd Laoveeravat, Priya R Abhyankar, Aaron R Brenner, Moamen M Gabr, Fadlallah G Habr, Amporn Atsawarungruangkit
Passisd Laoveeravat, Moamen M Gabr, Division of Digestive Diseases and Nutrition, University of Kentucky College of Medicine, Lexington, KY 40536, United States
Priya R Abhyankar, Aaron R Brenner, Department of Internal Medicine, University of Kentucky College of Medicine, Lexington, KY 40536, United States
Fadlallah G Habr, Amporn Atsawarungruangkit, Division of Gastroenterology, Warren Alpert Medical School of Brown University, Providence, RI 02903, United States
Author contributions: Laoveeravat P, Abhyankar PR, and Brenner AR equally contributed to this paper with conception and design of the study, literature review and analysis, drafting the manuscript; Gabr MM, Habr FG, and Atsawarungruangkit A provided critical revision, editing, and final approval of the final version.
Conflict-of-interest statement: No conflict of interest exists.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Amporn Atsawarungruangkit, MD, Academic Fellow, Instructor, Research Fellow, Division of Gastroenterology, Warren Alpert Medical School of Brown University, 593 Eddy Street, Providence, RI 02903, United States. amporn_atsawarungruangkit@brown.edu
Received: January 26, 2021 Peer-review started: January 26, 2021 First decision: February 27, 2021 Revised: March 31, 2021 Accepted: April 20, 2021 Article in press: April 20, 2021 Published online: April 28, 2021
Abstract
Artificial intelligence (AI) has been increasingly utilized in medical applications, especially in the field of gastroenterology. AI can assist gastroenterologists in imaging-based testing and prediction of clinical diagnosis, for examples, detecting polyps during colonoscopy, identifying small bowel lesions using capsule endoscopy images, and predicting liver diseases based on clinical parameters. With its high mortality rate, pancreatic cancer can highly benefit from AI since the early detection of small lesion is difficult with conventional imaging techniques and current biomarkers. Endoscopic ultrasound (EUS) is a main diagnostic tool with high sensitivity for pancreatic adenocarcinoma and pancreatic cystic lesion. The standard tumor markers have not been effective for diagnosis. There have been recent research studies in AI application in EUS and novel biomarkers to early detect and differentiate malignant pancreatic lesions. The findings are impressive compared to the available traditional methods. Herein, we aim to explore the utility of AI in EUS and novel serum and cyst fluid biomarkers for pancreatic cancer detection.
Core Tip: Artificial intelligence (AI) aided endoscopic ultrasound (EUS) and microRNA analyses are sensitive and effective for pancreatic cancer detection with sensitivity of more than 95%. The size of pancreatic lesion does not affect the diagnostic performance by artificial intelligence. This will help overcome the delayed diagnosis and high mortality of pancreatic cancer. Recent studies showed that the speed of AI system in EUS can be performed in real time fashion. This will be adjunctive to the conventional EUS examination for future utility.
