Retrospective Cohort Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Dec 8, 2023; 4(2): 18-26
Published online Dec 8, 2023. doi: 10.37126/aige.v4.i2.18
Artificial intelligence fails to improve colonoscopy quality: A single centre retrospective cohort study
Naeman Goetz, Katherine Hanigan, Richard Kai-Yuan Cheng
Naeman Goetz, Katherine Hanigan, Richard Kai-Yuan Cheng, Department of Gastroenterology, Redcliffe Hospital, Redcliffe 4020, Australia
Author contributions: All authors conceived the idea. KH collated the data from the departmental database; Goetz N performed the statistical analysis and was primarily responsible for writing the manuscript; Cheng RKY provided substantial revisions to the manuscript.
Institutional review board statement: The study was approved by the local Human Research Ethics Committee (HREC/2023/MNHA/100582).
Informed consent statement: A waiver of consent was obtained from the HREC.
Conflict-of-interest statement: The authors declare there is no potential sources of conflict of interest. This study was not funded, with research work conducted in-kind.
Data sharing statement: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
STROBE statement: The authors have read the STROBE Statement – checklist of items, and the manuscript was prepared and revised according to the STROBE Statement – checklist of items.
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: Naeman Goetz, BSc, MD, Doctor, Department of Gastroenterology, Redcliffe Hospital, Anzac Ave, Redcliffe QLD 4020, Redcliffe 4020, Australia. naeman.goetz@health.qld.gov.au
Received: September 4, 2023
Peer-review started: September 4, 2023
First decision: November 1, 2023
Revised: November 7, 2023
Accepted: November 30, 2023
Article in press: November 30, 2023
Published online: December 8, 2023
Abstract
BACKGROUND

Limited data currently exists on the clinical utility of Artificial Intelligence Assisted Colonoscopy (AIAC) outside of clinical trials.

AIM

To evaluate the impact of AIAC on key markers of colonoscopy quality compared to conventional colonoscopy (CC).

METHODS

This single-centre retrospective observational cohort study included all patients undergoing colonoscopy at a secondary centre in Brisbane, Australia. CC outcomes between October 2021 and October 2022 were compared with AIAC outcomes after the introduction of the Olympus Endo-AID module from October 2022 to January 2023. Endoscopists who conducted over 50 procedures before and after AIAC introduction were included. Procedures for surveillance of inflammatory bowel disease were excluded. Patient demographics, proceduralist specialisation, indication for colonoscopy, and colonoscopy quality metrics were collected. Adenoma detection rate (ADR) and sessile serrated lesion detection rate (SSLDR) were calculated for both AIAC and CC.

RESULTS

The study included 746 AIAC procedures and 2162 CC procedures performed by seven endoscopists. Baseline patient demographics were similar, with median age of 60 years with a slight female predominance (52.1%). Procedure indications, bowel preparation quality, and caecal intubation rates were comparable between groups. AIAC had a slightly longer withdrawal time compared to CC, but the difference was not statistically significant. The introduction of AIAC did not significantly change ADR (52.1% for AIAC vs 52.6% for CC, P = 0.91) or SSLDR (17.4% for AIAC vs 18.1% for CC, P = 0.44).

CONCLUSION

The implementation of AIAC failed to improve key markers of colonoscopy quality, including ADR, SSLDR and withdrawal time. Further research is required to assess the utility and cost-efficiency of AIAC for high performing endoscopists.

Keywords: Artificial intelligence, Colonoscopy quality, Adenoma detection rate, Sessile serrated lesion detection rate, Withdrawal time

Core Tip: This paper investigates the utility of Artificial Intelligence Assisted Colonoscopy (AIAC) in enhancing colonoscopy quality, particularly adenoma detection rate. Using a retrospective design, we compare AIAC with conventional colonoscopy in a real-world setting, finding no significant improvement in surrogate markers of colonoscopy quality. We explore challenges in artificial intelligence-human interaction and emphasise the need for further validation.