Literature DB >> 34530161

Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial).

Jeremy R Glissen Brown1, Nabil M Mansour2, Pu Wang3, Maria Aguilera Chuchuca4, Scott B Minchenberg5, Madhuri Chandnani4, Lin Liu6, Seth A Gross7, Neil Sengupta8, Tyler M Berzin4.   

Abstract

BACKGROUND & AIMS: Artificial intelligence-based computer-aided polyp detection (CADe) systems are intended to address the issue of missed polyps during colonoscopy. The effect of CADe during screening and surveillance colonoscopy has not previously been studied in a United States (U.S.) population.
METHODS: We conducted a prospective, multi-center, single-blind randomized tandem colonoscopy study to evaluate a deep-learning based CADe system (EndoScreener, Shanghai Wision AI, China). Patients were enrolled across 4 U.S. academic medical centers from 2019 through 2020. Patients presenting for colorectal cancer screening or surveillance were randomized to CADe colonoscopy first or high-definition white light (HDWL) colonoscopy first, followed immediately by the other procedure in tandem fashion by the same endoscopist. The primary outcome was adenoma miss rate (AMR), and secondary outcomes included sessile serrated lesion (SSL) miss rate and adenomas per colonoscopy (APC).
RESULTS: A total of 232 patients entered the study, with 116 patients randomized to undergo CADe colonoscopy first and 116 patients randomized to undergo HDWL colonoscopy first. After the exclusion of 9 patients, the study cohort included 223 patients. AMR was lower in the CADe-first group compared with the HDWL-first group (20.12% [34/169] vs 31.25% [45/144]; odds ratio [OR], 1.8048; 95% confidence interval [CI], 1.0780-3.0217; P = .0247). SSL miss rate was lower in the CADe-first group (7.14% [1/14]) vs the HDWL-first group (42.11% [8/19]; P = .0482). First-pass APC was higher in the CADe-first group (1.19 [standard deviation (SD), 2.03] vs 0.90 [SD, 1.55]; P = .0323). First-pass ADR was 50.44% in the CADe-first group and 43.64 % in the HDWL-first group (P = .3091).
CONCLUSION: In this U.S. multicenter tandem colonoscopy randomized controlled trial, we demonstrate a decrease in AMR and SSL miss rate and an increase in first-pass APC with the use of a CADe-system when compared with HDWL colonoscopy alone.
Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adenoma Detection Rate; Adenoma Miss Rate; Computer-aided Detection; Deep Learning; Randomized Tandem Colonoscopy Study

Mesh:

Year:  2021        PMID: 34530161     DOI: 10.1016/j.cgh.2021.09.009

Source DB:  PubMed          Journal:  Clin Gastroenterol Hepatol        ISSN: 1542-3565            Impact factor:   13.576


  9 in total

Review 1.  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

2.  Using of artificial intelligence: Current and future applications in colorectal cancer screening.

Authors:  Georgios Zacharakis; Abdulaziz Almasoud
Journal:  World J Gastroenterol       Date:  2022-06-28       Impact factor: 5.374

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.  Real-time artificial intelligence (AI)-aided endoscopy improves adenoma detection rates even in experienced endoscopists: a cohort study in Singapore.

Authors:  Frederick H Koh; Jasmine Ladlad; Eng-Kiong Teo; Cui-Li Lin; Fung-Joon Foo
Journal:  Surg Endosc       Date:  2022-07-26       Impact factor: 3.453

Review 5.  Artificial intelligence in colonoscopy: A review on the current status.

Authors:  Solveig Linnea Veen Larsen; Yuichi Mori
Journal:  DEN open       Date:  2022-03-23

Review 6.  Randomized Controlled Trials of Artificial Intelligence in Clinical Practice: Systematic Review.

Authors:  Thomas Y T Lam; Max F K Cheung; Yasmin L Munro; Kong Meng Lim; Dennis Shung; Joseph J Y Sung
Journal:  J Med Internet Res       Date:  2022-08-25       Impact factor: 7.076

7.  Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study.

Authors:  Quchuan Zhao; Qing Jia; Tianyu Chi
Journal:  BMC Gastroenterol       Date:  2022-07-25       Impact factor: 2.847

8.  Computer copilots for endoscopic diagnosis.

Authors:  James A Diao; Joseph C Kvedar
Journal:  NPJ Digit Med       Date:  2022-09-01

9.  Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review.

Authors:  Deborah Plana; Dennis L Shung; Alyssa A Grimshaw; Anurag Saraf; Joseph J Y Sung; Benjamin H Kann
Journal:  JAMA Netw Open       Date:  2022-09-01
  9 in total

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