Chow KW, Bell MT, Cumpian N, Amour M, Hsu RH, Eysselein VE, Srivastava N, Fleischman MW, Reicher S. Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy. World J Gastrointest Endosc 2024; 16(6): 335-342 [PMID: 38946853 DOI: 10.4253/wjge.v16.i6.335]
Corresponding Author of This Article
Kenneth W Chow, MD, Doctor, Researcher, Department of Medicine, Harbor-UCLA Medical Center, 1000 W Carson Street, Torrance, CA 90502, United States. kwchow555@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
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/
World J Gastrointest Endosc. Jun 16, 2024; 16(6): 335-342 Published online Jun 16, 2024. doi: 10.4253/wjge.v16.i6.335
Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy
Kenneth W Chow, Matthew T Bell, Nicholas Cumpian, Maryanne Amour, Ryan H Hsu, Viktor E Eysselein, Neetika Srivastava, Michael W Fleischman, Sofiya Reicher
Kenneth W Chow, Matthew T Bell, Nicholas Cumpian, Maryanne Amour, Department of Medicine, Harbor-UCLA Medical Center, Torrance, CA 90502, United States
Ryan H Hsu, Department of Bioengineering, Jacobs School of Engineering, University of California, San Diego, La Jolla, CA 92093, United States
Viktor E Eysselein, Neetika Srivastava, Michael W Fleischman, Sofiya Reicher, Department of Gastroenterology, Harbor-UCLA Medical Center, Torrance, CA 90502, United States
Co-corresponding authors: Kenneth W Chow and Sofiya Reicher.
Author contributions: Study concept, study supervision, and design was performed by Reicher S; acquisition of data was performed by Chow KW, Bell MT, Cumpian N, and Amour M; analysis and interpretation of the data was performed by Chow KW, Hsu RH, Eysselein VE, Srivastava N, Fleischman MW, and Reicher S; statistical analysis was performed by Chow KW and Hsu RH; drafting of the manuscript was performed by Chow KW; all authors have read and approve the final manuscript.
Institutional review board statement: This study was approved by the Institutional Review Board (IRB number: 18CR-31902-01) of the Lundquist Institute at Harbor-UCLA.
Informed consent statement: Informed consent was waived by the Institutional Review Board.
Conflict-of-interest statement: Sofiya Reicher has served as a consultant for Boston Scientific. The rest of the authors have no conflicts of interest to disclose.
Data sharing statement: No additional data are available.
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: Kenneth W Chow, MD, Doctor, Researcher, Department of Medicine, Harbor-UCLA Medical Center, 1000 W Carson Street, Torrance, CA 90502, United States. kwchow555@gmail.com
Received: February 28, 2024 Revised: April 16, 2024 Accepted: April 28, 2024 Published online: June 16, 2024 Processing time: 107 Days and 7.8 Hours
Abstract
BACKGROUND
Improved adenoma detection rate (ADR) has been demonstrated with artificial intelligence (AI)-assisted colonoscopy. However, data on the real-world application of AI and its effect on colorectal cancer (CRC) screening outcomes is limited.
AIM
To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or symptoms.
METHODS
AI software (GI Genius, Medtronic) was implemented into the standard procedure protocol in November 2022. Data was collected on patient demographics, procedure indication, polyp size, location, and pathology. CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up.
RESULTS
We evaluated 1008 colonoscopies (278 pre-AI, 255 early post-AI, 285 established post-AI, and 190 late post-AI). The ADR was 38.1% pre-AI, 42.0% early post-AI (P = 0.77), 40.0% established post-AI (P = 0.44), and 39.5% late post-AI (P = 0.77). There were no significant differences in polyp detection rate (PDR, baseline 59.7%), advanced ADR (baseline 16.2%), and non-neoplastic PDR (baseline 30.0%) before and after AI introduction.
CONCLUSION
In patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy. Although the potential of AI in colonoscopy is undisputed, current AI technology may not universally elevate screening metrics across all situations and patient populations. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.
Core Tip: This study analyzed the long-term impact of artificial intelligence (AI)-assisted colonoscopy in a diverse at-risk patient population undergoing diagnostic colonoscopy for positive colorectal cancer (CRC) screening tests or symptoms. It was found that in patients with an increased pre-test probability of having an abnormal colonoscopy, the current generation of AI did not yield enhanced screening metrics over high-quality colonoscopy. Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.
