Cited by 231Colorectal Cancer Screening and DetectionAI in cancer detectionGenetic factors in colorectal cancer

Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study

Wang, Pu, Liu, Peixi, Glissen Brown, Jeremy R., Berzin, Tyler M., Zhou, Guanyu, Lei, Shan, Liu, Xiaogang, Li, Liangping +1 more·2020·Gastroenterology

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…

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Crossref · 10.1053/j.gastro.2020.06.023

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References 43 · Cited by 231

Authors
Wang, P., Liu, P., Glissen Brown, J. R., Berzin, T. M., Zhou, G., Lei, S., Liu, X., Li, L., & Xiao, X.
Affiliations
  • Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (CN)
  • Beth Israel Deaconess Medical Center (US)
Corresponding author
Xiao, Xun
Venue
Gastroenterology
Publisher
Elsevier BV
Published
2020-10
Volume
159
Issue
4
Pages
1252-1261.e5
ISSN
0016-5085 (ISSN-L)
OpenAlex
W3036377470
Cited by
231
References
43
Citations by year
2026: 28 · 2025: 31 · 2024: 38 · 2023: 49 · 2022: 43 · 2021: 31 · 2020: 9 · 2017: 1
FWCI
11.98
Type
journal article
Language
en
Open access
closed
Locations
  • Gastroenterology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Health Sciences › Medicine › Pathology and Forensic Medicine › Genetic factors in colorectal cancer
Keywords
computer-aided detection, colonoscopy, adenoma miss rate, polyp miss rate, white light colonoscopy, deep learning, colorectal cancer screening, tandem colonoscopy, missed polyps, precancerous polyps, colonoscopy quality improvement
MeSH terms
Deep Learning, Missed Diagnosis, Adolescent, Adult, Aged, China, Colonic Neoplasms, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Reproducibility of Results, Adenomatous Polyps, Young Adult

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