Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study
Abstract
Background Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system that avoids potential operational bias.Methods We did a double-blind randomised trial at the endoscopy centre in Caotang branch hospital of Sichuan Provincial People's Hospital in China. We enrolled consecutive patients (aged 18-75 years) presenting for diagnostic and screening colonoscopy. We excluded patients with a history of inflammatory bowel disease, colorectal cancer, or colorectal surgery or who had a contraindication for biopsy; we also excluded patients who had previously had an unsuccessful colonoscopy and who had a high suspicion for polyposis syndromes, inflammatory bowel disease, and colorectal cancer. We allocated patients (1:1) to colonoscopy with either the CADe system or a sham system. Randomisation was by computer-generated random number allocation. Patients and the endoscopist were unaware of the random assignment. To achieve masking, the output of the system was shown on a second monitor that was only visible to an observer who was responsible for reporting the alerts. The primary outcome was the adenoma detection rate (ADR), which is the proportion of individuals having a complete colonoscopy, from caecum to rectum, who had one or more adenomas detected. The primary analysis was per protocol. We also analysed characteristics of polyps and adenomas missed initially by endoscopists but detected by the CADe system. This trial is complete and is registered with http://www.chictr.org.cn, ChiCTR1800017675.Findings Between Sept 3, 2018, and Jan 11, 2019, 1046 patients were enrolled to the study, of whom 36 were excluded before randomisation, 508 were allocated colonoscopy with polyp detection using the CADe system, and 502 were allocated colonoscopy with the sham system. After further excluding patients who met exclusion criteria, 484 patients in the CADe group and 478 in the sham group were included in analyses. The ADR was significantly greater in the CADe group than in the sham group, with 165 (34%) of 484 patients allocated to the CADe system having one or more adenomas detected versus 132 (28%) of 478 allocated to the sham system (odds ratio 1·36, 95% CI 1·03-1·79; p=0·030). No complications were reported among all colonoscopy procedures. Polyps initially missed by the endoscopist but identified by the CADe system were generally small in size, isochromatic, flat in shape, had an unclear boundary, were partly behind colon folds, and were on the edge of the visual field.Interpretation Polyps initially missed by the endoscopist had characteristics that are sometimes difficult for skilled endoscopists to recognise. Such polyps could be detected using a high-performance CADe system during colonoscopy. The effect of CADe during colonoscopy on the incidence of interval colorectal cancer should be investigated.Funding None.
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In Wang et al. 2020 double-blind trial, the CADe group had a higher PDR (52%) than the control group (37%) (p<0.0001).
“PDR was also higher (52%) than the control group (37%) (p<0·0001).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [18]
In Wang et al. 2020 double-blind trial, the CADe group had a higher ADR (34%) than the control group (28%) (p=0.030).
“The CADe group displayed a significantly higher ADR (34%) compared to the control group (28%) (p=0·030).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [18]
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 [18]
Overall, evidence strongly supports adoption of AI for improving lesion detection during colonoscopy.
“Overall, the evidence strongly supports the adoption of AI for improving lesion detection during colonoscopy ( Tables 3 and 4 ) [76 , 78-81] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [80]
Multiple randomized controlled trials of AI polyp detection in colonoscopy have reported promising results.
“multiple randomized controlled trials have also reported promising results (16)(17)(18)(19)(20)(21)(22).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [16]
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“In the same year, Wang et al. [18] conducted a double-blind randomized trial involving 962 participants to assess the CADe system's efficiency in identifying colon polyps and adenomas during colonoscopies.”
Background
- Authors
- Wang, P., Liu, X., Berzin, T. M., Glissen Brown, J. R., Liu, P., Zhou, C., Lei, L., Li, L., Guo, Z., Lei, S., Xiong, F., Wang, H., Song, Y., Pan, Y., & Zhou, G.
- Affiliations
- Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital (CN)
- Beth Israel Deaconess Medical Center (US)
- Corresponding author
- Zhou, Guanyu
- ORCID
- Venue
- The Lancet Gastroenterology & Hepatology
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 5
- Issue
- 4
- Pages
- 343-351
- ISSN
- 2468-1156 (ISSN-L)
- 2468-1253
- OpenAlex
- W3001365238
- Cited by
- 415
- References
- 35
- Citations by year
- 2026: 35 · 2025: 50 · 2024: 58 · 2023: 76 · 2022: 59 · 2021: 82 · 2020: 55
- FWCI
- 24.56
- Type
- journal article
- Language
- en
- Update notices
- erratum (2020-04-01)
- Open access
- closed
- Locations
- The Lancet. Gastroenterology & hepatology (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 › Oncology › Colorectal Cancer Surgical Treatments
- Keywords
- computer-aided detection, colonoscopy, colorectal adenoma detection, deep learning, polyp detection, colorectal cancer screening, missed adenomas, colorectal cancer prevention, colorectal polyps, colorectal cancer screening guidelines
- MeSH terms
- Deep Learning, Adenoma, Adult, China, Colonic Polyps, Colonoscopy, Diagnosis, Computer-Assisted, Double-Blind Method, Female, Humans, Male, Mass Screening, Middle Aged, Colorectal Neoplasms, Observer Variation, Incidence, Case-Control Studies, Early Diagnosis, Clinical Alarms
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