Effect of an artificial intelligence-based quality improvement system on efficacy of a computer-aided detection system in colonoscopy: a four-group parallel study
Abstract
Background Tandem colonoscopy studies have found that about one in five adenomas are missed at colonoscopy. It remains debatable whether the combination of a computer-aided polyp detection (CADe) system with a computer-aided quality improvement (CAQ) system for real-time monitoring of withdrawal speed results in additional benefits in adenoma detection or if the synergetic effect may be harmed due to excessive visual burden resulting from information overload. This study aimed to evaluate the interaction effect on improving the adenoma detection rate (ADR). Methods This single-center, randomized, four-group, parallel, controlled study was performed at Renmin Hospital of Wuhan University. Between 1 July and 15 October 2020, 1076 patients were randomly allocated into four treatment groups: control 271, CADe 268, CAQ 269, and CADe plus CAQ (COMBO) 268. The primary outcome was ADR. Results The ADR in the control, CADe, CAQ, and COMBO groups was 14.76 % (95 % confidence interval [CI] 10.54 to 18.98), 21.27 % (95 %CI 16.37 to 26.17), 24.54 % (95 %CI 19.39 to 29.68), and 30.60 % (95 %CI 25.08 to 36.11), respectively. The ADR was higher in the COMBO group compared with the CADe group (21.27 % vs. 30.6 %, P = 0.024, odds ratio [OR] 1.284, 95 %CI 1.033 to 1.596) but not compared with the CAQ group (24.54 % vs. 30.6 %, P = 0.213, OR 1.309, 95 %CI 0.857 to 2.000, respectively). Conclusions CAQ significantly improved the efficacy of CADe in a four-group, parallel, controlled study. No sig…
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In Yao et al. 2022, the COMBO group had a notably higher ADR than the CADe-alone group.
“The findings indicated that the group utilizing both CADe and CAQ systems displayed a notably higher ADR compared to the group using CADe alone.”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [21]
In Yao et al. 2022, the ADRs were 14.76%, 21.27%, 24.54%, and 30.60% in the control, CADe, CAQ, and COMBO groups, respectively.
“The ADRs were 14.76%, 21.27%, 24.54%, and 30.60% in the control, CADe, CAQ, and COMBO groups, respectively.”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [21]
In Yao et al. 2022, both CADe and CAQ groups had notably higher ADRs than the control group.
“In comparison to the control group, both the CADe and CAQ groups exhibited notably higher ADRs.”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [21]
Only two studies were designed as double-blinded RCTs, and only one of them was a truly double-blinded RCT.
“Only two studies [18,21] were designed as double-blinded RCTs, and only one of them was a truly double-blinded RCT.”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [21]
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References 26 · Cited by 116
- Authors
- Yao, L., Zhang, L., Liu, J., Zhou, W., He, C., Zhang, J., Wu, L., Wang, H., Xu, Y., Gong, D., Xu, M., Li, X., Bai, Y., Gong, R., Sharma, P., & Yu, H.
- Affiliations
- Renmin Hospital of Wuhan University (CN)
- Department of Gastroenterology, Jilin Renmin Hospital, Jilin, China
- University of Kansas (US)
- ORCID
- Venue
- Endoscopy
- Publisher
- Georg Thieme Verlag KG
- Published
- 2021-11-25
- Volume
- 54
- Issue
- 08
- Pages
- 757-768
- ISSN
- 0013-726X (ISSN-L)
- 1438-8812
- OpenAlex
- W3216625415
- Cited by
- 116
- References
- 26
- Citations by year
- 2026: 19 · 2025: 31 · 2024: 25 · 2023: 31 · 2022: 10
- FWCI
- 6.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Endoscopy (publishedVersion)
- PubMed (publishedVersion)
- Funders
- the Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incisio (2018BCC337)
- Hubei Province Major Science and Technology Innovation Project (2018–916–000–008)
- 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
- computer-aided detection, colonoscopy, adenoma detection rate, polyp detection, artificial intelligence, tandem colonoscopy, missed adenomas, information overload
- MeSH terms
- Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Humans, Colorectal Neoplasms, Quality Improvement
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