Cited by 235Colorectal Cancer Screening and DetectionAI in cancer detectionColorectal Cancer Surgical Treatments

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

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Glissen Brown, Jeremy R., Mansour, Nabil M., Wang, Pu, Chuchuca, Maria Aguilera, Minchenberg, Scott B., Chandnani, Madhuri, Liu, Lin, Gross, Seth A., Sengupta, Neil, Berzin, Tyler M.·2022·Clinical Gastroenterology and Hepatology

DOI 10.1016/j.cgh.2021.09.009

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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.

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Authors: Glissen Brown, J. R., Mansour, N. M., Wang, P., Chuchuca, M. A., Minchenberg, S. B., Chandnani, M., Liu, L., Gross, S. A., Sengupta, N., & Berzin, T. M.
Affiliations: Baylor College of Medicine (US)
Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (CN)
Beth Israel Deaconess Medical Center (US)
Shanghai Jiao Tong University (CN)
NYU Langone Health (US)
University of Chicago (US)
ORCID: Mansour, Nabil M.
Wang, Pu
Minchenberg, Scott B.
Chandnani, Madhuri
Liu, Lin
Gross, Seth A.
Sengupta, Neil
Berzin, Tyler M.
Venue: Clinical Gastroenterology and Hepatology
Publisher: Elsevier BV
Published: 2022-07
Volume: 20
Issue: 7
Pages: 1499-1507.e4
ISSN: 1542-3565 (ISSN-L)
DOI: 10.1016/j.cgh.2021.09.009
OpenAlex: W3199688237
Cited by: 235
References: 29
Citations by year: 2026: 43 · 2025: 65 · 2024: 46 · 2023: 49 · 2022: 32
FWCI: 11.57
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Clinical Gastroenterology and Hepatology (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (12090024)
Science and Technology Commission of Shanghai Municipality (2021SHZDZX0102)
Natural Science Foundation of Shanghai (21ZR1431000)
Topics: Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Oncology › Colorectal Cancer Surgical Treatments
Keywords: deep learning, colonoscopy, adenoma miss rate, colorectal cancer screening, surveillance colonoscopy, sessile serrated lesions, adenoma detection rate, tandem colonoscopy, artificial intelligence in endoscopy, colorectal adenoma, polyp detection, United States population
MeSH terms: Deep Learning, Missed Diagnosis, Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Humans, Prospective Studies, United States, Colorectal Neoplasms, Single-Blind Method

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

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