Literature DB >> 32562721

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

Pu Wang1, Peixi Liu1, Jeremy R Glissen Brown2, Tyler M Berzin2, Guanyu Zhou1, Shan Lei1, Xiaogang Liu1, Liangping Li1, Xun Xiao3.   

Abstract

BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could be polyps that appear on-screen but are not recognized by endoscopists or polyps that are in locations that do not appear on the screen at all. Computer-aided detection (CADe) systems, based on deep learning, might reduce rates of missed adenomas by displaying visual alerts that identify precancerous polyps on the endoscopy monitor in real time. We compared adenoma miss rates of CADe colonoscopy vs routine white-light colonoscopy.
METHODS: We performed a prospective study of patients, 18-75 years old, referred for diagnostic, screening, or surveillance colonoscopies at a single endoscopy center of Sichuan Provincial People's Hospital from June 3, 2019 through September 24, 2019. Same day, tandem colonoscopies were performed for each participant by the same endoscopist. Patients were randomly assigned to groups that received either CADe colonoscopy (n=184) or routine colonoscopy (n=185) first, followed immediately by the other procedure. Endoscopists were blinded to the group each patient was assigned to until immediately before the start of each colonoscopy. Polyps that were missed by the CADe system but detected by endoscopists were classified as missed polyps. False polyps were those continuously traced by the CADe system but then determined not to be polyps by the endoscopists. The primary endpoint was adenoma miss rate, which was defined as the number of adenomas detected in the second-pass colonoscopy divided by the total number of adenomas detected in both passes.
RESULTS: The adenoma miss rate was significantly lower with CADe colonoscopy (13.89%; 95% CI, 8.24%-19.54%) than with routine colonoscopy (40.00%; 95% CI, 31.23%-48.77%, P<.0001). The polyp miss rate was significantly lower with CADe colonoscopy (12.98%; 95% CI, 9.08%-16.88%) than with routine colonoscopy (45.90%; 95% CI, 39.65%-52.15%) (P<.0001). Adenoma miss rates in ascending, transverse, and descending colon were significantly lower with CADe colonoscopy than with routine colonoscopy (ascending colon 6.67% vs 39.13%; P=.0095; transverse colon 16.33% vs 45.16%; P=.0065; and descending colon 12.50% vs 40.91%, P=.0364).
CONCLUSIONS: CADe colonoscopy reduced the overall miss rate of adenomas by endoscopists using white-light endoscopy. Routine use of CADe might reduce the incidence of interval colon cancers. chictr.org.cn study no: ChiCTR1900023086.
Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  AMR; Artificial Intelligence; Early Detection; Neoplasm

Mesh:

Year:  2020        PMID: 32562721     DOI: 10.1053/j.gastro.2020.06.023

Source DB:  PubMed          Journal:  Gastroenterology        ISSN: 0016-5085            Impact factor:   22.682


  21 in total

Review 1.  Current status and limitations of artificial intelligence in colonoscopy.

Authors:  Alexander Hann; Joel Troya; Daniel Fitting
Journal:  United European Gastroenterol J       Date:  2021-06-07       Impact factor: 4.623

Review 2.  Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy.

Authors:  Yu Kamitani; Kouichi Nonaka; Hajime Isomoto
Journal:  J Clin Med       Date:  2022-05-22       Impact factor: 4.964

Review 3.  Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications.

Authors:  James Weiquan Li; Lai Mun Wang; Tiing Leong Ang
Journal:  Singapore Med J       Date:  2022-03       Impact factor: 3.331

4.  The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy: a prospective randomized study.

Authors:  Peixi Liu; Pu Wang; Jeremy R Glissen Brown; Tyler M Berzin; Guanyu Zhou; Weihui Liu; Xun Xiao; Ziyang Chen; Zhihong Zhang; Chao Zhou; Lei Lei; Fei Xiong; Liangping Li; Xiaogang Liu
Journal:  Therap Adv Gastroenterol       Date:  2020-12-15       Impact factor: 4.409

Review 5.  Artificial intelligence-aided colonoscopy: Recent developments and future perspectives.

Authors:  Giulio Antonelli; Paraskevas Gkolfakis; Georgios Tziatzios; Ioannis S Papanikolaou; Konstantinos Triantafyllou; Cesare Hassan
Journal:  World J Gastroenterol       Date:  2020-12-21       Impact factor: 5.742

6.  Artificial intelligence in gastrointestinal endoscopy.

Authors:  Rahul Pannala; Kumar Krishnan; Joshua Melson; Mansour A Parsi; Allison R Schulman; Shelby Sullivan; Guru Trikudanathan; Arvind J Trindade; Rabindra R Watson; John T Maple; David R Lichtenstein
Journal:  VideoGIE       Date:  2020-11-09

Review 7.  Artificial Intelligence in Endoscopy.

Authors:  Yutaka Okagawa; Seiichiro Abe; Masayoshi Yamada; Ichiro Oda; Yutaka Saito
Journal:  Dig Dis Sci       Date:  2021-06-21       Impact factor: 3.199

Review 8.  Is artificial intelligence the final answer to missed polyps in colonoscopy?

Authors:  Thomas K L Lui; Wai K Leung
Journal:  World J Gastroenterol       Date:  2020-09-21       Impact factor: 5.742

9.  Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method.

Authors:  Omer F Ahmad; Yuichi Mori; Masashi Misawa; Shin-Ei Kudo; John T Anderson; Jorge Bernal; Tyler M Berzin; Raf Bisschops; Michael F Byrne; Peng-Jen Chen; James E East; Tom Eelbode; Daniel S Elson; Suryakanth R Gurudu; Aymeric Histace; William E Karnes; Alessandro Repici; Rajvinder Singh; Pietro Valdastri; Michael B Wallace; Pu Wang; Danail Stoyanov; Laurence B Lovat
Journal:  Endoscopy       Date:  2021-01-13       Impact factor: 9.776

Review 10.  Artificial intelligence-assisted colonoscopy: A review of current state of practice and research.

Authors:  Mahsa Taghiakbari; Yuichi Mori; Daniel von Renteln
Journal:  World J Gastroenterol       Date:  2021-12-21       Impact factor: 5.742

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