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Artif Intell Gastrointest Endosc. Aug 28, 2021; 2(4): 179-184
Published online Aug 28, 2021. doi: 10.37126/aige.v2.i4.179
Artificial intelligence as a means to improve recognition of gastrointestinal angiodysplasia in video capsule endoscopy
Gerald A Cox II, Christian S Jackson, Kenneth J Vega
Gerald A Cox II, Department of Medicine, Loma Linda University Medical Center, Loma Linda, CA 92354, United States
Christian S Jackson, Gastroenterology Section, VA Loma Linda Healthcare System, Loma Linda, CA 92357, United States
Kenneth J Vega, Division of Gastroenterology and Hepatology, Augusta University - Medical College of Georgia, Augusta, GA 30912, United States
Author contributions: Cox II GA, Jackson CS, and Vega KJ planned the minireview and reviewed all data included, wrote the minireview and revised it for intellectual content; Vega KJ is the editorial guarantor; All authors approved the final submitted version.
Conflict-of-interest statement: No conflict of interest exists for all authors of this manuscript.
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: Kenneth J Vega, MD, Director, Professor, Division of Gastroenterology and Hepatology, Augusta University - Medical College of Georgia, 1120 15th Street, AD 2226, Augusta, GA 30912, United States. kvega@augusta.edu
Received: June 3, 2021
Peer-review started: June 3, 2021
First decision: June 23, 2021
Revised: July 7, 2021
Accepted: August 13, 2021
Article in press: August 13, 2021
Published online: August 28, 2021
Processing time: 94 Days and 14.3 Hours
Abstract

Gastrointestinal angiodysplasia (GIAD) is defined as the pathological process where blood vessels, typically venules and capillaries, become engorged, tortuous and thin walled – which then form arteriovenous connections within the mucosal and submucosal layers of the gastrointestinal tract. GIADs are a significant cause of gastrointestinal bleeding and are the main cause for suspected small bowel bleeding. To make the diagnosis, gastroenterologists rely on the use of video capsule endoscopy (VCE) to “target” GIAD. However, the use of VCE can be cumbersome secondary to reader fatigue, suboptimal preparation, and difficulty in distinguishing images. The human eye is imperfect. The same capsule study read by two different readers are noted to have miss rates like other forms of endoscopy. Artificial intelligence (AI) has been a means to bridge the gap between human imperfection and recognition of GIAD. The use of AI in VCE have shown that detection has improved, however the other burdens and limitations still need to be addressed. The use of AI for the diagnosis of GIAD shows promise and the changes needed to enhance the current practice of VCE are near.

Keywords: Artificial intelligence; Video capsule endoscopy; Gastrointestinal angiodysplasia; Detection; Bleeding; Small bowel

Core Tip: Video capsule endoscopy (VCE) is the primary modality to diagnose gastrointestinal angiodysplasias (GIADs). Typically, gastroenterologists rely on VCE to make a diagnosis of GIAD prior to referral for deep enteroscopy. However, VCE analysis can be cumbersome secondary to reader fatigue, suboptimal preparation, and difficulty in distinguishing images. Use of artificial intelligence in VCE has shown improved GIAD detection, however limitations exist that still need to be addressed. The use of artificial intelligence for GIAD diagnosis shows promise and changes needed to enhance current VCE practices are near.