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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)
Open accessAbstract
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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“PMR was also lower (20.7%) than the control group (33.71%) (p=0.0007).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [22]
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“The study involved 223 participants, with the CADe group showing a significantly lower AMR (20.12%) compared to the control group (31.25%) (p=0.0247).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [22]
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A mesterséges intelligencia szerepe a kolonoszkópiában
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Metadata
- 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
- Venue
- Clinical Gastroenterology and Hepatology
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 20
- Issue
- 7
- Pages
- 1499-1507.e4
- ISSN
- 1542-3565 (ISSN-L)
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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