Effect of Real-Time Computer-Aided Detection of Colorectal Adenoma in Routine Colonoscopy (COLO-GENIUS): A Single-Centre Randomised Controlled Trial
Abstract
Background Artificial intelligence systems have been developed to improve polyp detection. We aimed to evaluate the effect of real-time computer-aided detection (CADe) on the adenoma detection rate (ADR) in routine colonoscopy.Methods This single-centre randomised controlled trial (COLO-GENIUS) was done at the Digestive Endoscopy Unit, Pôle Digestif Paris-Bercy, Clinique Paris-Bercy, Charenton-le-Pont, France. All consecutive individuals aged 18 years or older who were scheduled for a total colonoscopy and had an American Society of Anesthesiologists score of 1-3 were screened for inclusion. After the caecum was reached and the colonic preparation was appropriate, eligible participants were randomly assigned (1:1; computer-generated random numbers list) to either standard colonoscopy or CADe-assisted colonoscopy (GI Genius 2.0.2; Medtronic). Participants and cytopathologists were masked to study assignment, whereas endoscopists were not. The primary outcome was ADR, which was assessed in the modified intention-to-treat population (all randomly assigned participants except those with misplaced consent forms). Safety was analysed in all included patients. According to statistical calculations, 20 endoscopists from the Clinique Paris-Bercy had to include approximately 2100 participants with 1:1 randomisation. The trial is complete and registered with ClinicalTrials.gov, NCT04440865.Findings Between May 1, 2021, and May 1, 2022, 2592 participants were assessed for eligibility, of whom 2039 were randomly assigned to standard colonoscopy (n=1026) or CADe-assisted colonoscopy (n=1013). 14 participants in the standard group and ten participants in the CADe group were then excluded due to misplaced consent forms, leaving 2015 participants (979 [48·6%] men and 1036 [51·4%] women) in the modified intention-to-treat analysis. ADR was 33·7% (341 of 1012 colonoscopies) in the standard group and 37·5% (376 of 1003 colonoscopies) in the CADe group (estimated mean absolute difference 4·1 percentage points [95% CI 0·0-8·1]; p=0·051). One bleeding event without deglobulisation occurred in the CADe group after a large (>2 cm) polyp resection and resolved after a haemostasis clip was placed during a second colonoscopy.Interpretation Our findings support the benefits of CADe, even in a non-academic centre. Systematic use of CADe in routine colonoscopy should be considered.Funding None.
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In Karsenti et al. 2023, the CADe group had a higher PDR (45%) than the control group (41%) (p=0.048).
“PDR was also higher (45%) than the control group (41%) (p=0·048).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [25]
In Karsenti et al. 2023, the CADe group had a higher ADR (37.5%) than the control group (33.7%) (p=0.051).
“The CADe group showed a higher ADR (37·5%) compared to the control group (33·7%) (p=0·051).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [25]
Natural language processing increases productivity in clinical oncology by converting unstructured text into analyzable data and standardizing documentation.
“Natural language processing (NLP) increases productivity in clinical oncology by converting unstructured text into analyzable data and standardizing documentation [41,42,43,44,45,46].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [42]
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 [18]
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- Authors
- Karsenti, D., Tharsis, G., Perrot, B., Cattan, P., Percie du Sert, A., Venezia, F., Zrihen, E., Gillet, A., Lab, J.-P., Tordjman, G., & Cavicchi, M.
- Affiliations
- Inserm (FR)
- Corresponding author
- Karsenti, David
- Venue
- The Lancet Gastroenterology & Hepatology
- Publisher
- Elsevier BV
- Published
- 2023-08
- Volume
- 8
- Issue
- 8
- Pages
- 726-734
- ISSN
- 2468-1156 (ISSN-L)
- 2468-1253
- OpenAlex
- W4379014664
- Cited by
- 62
- References
- 41
- Citations by year
- 2026: 18 · 2025: 16 · 2024: 21 · 2023: 7
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- The Lancet. Gastroenterology & hepatology (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Medtronic
- 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, colorectal adenoma, adenoma detection rate, routine colonoscopy, artificial intelligence, GI Genius, colorectal cancer screening, colorectal polyp detection, colorectal cancer prevention, colorectal cancer risk stratification, endoscopist training
- MeSH terms
- Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Computers, Female, Humans, Male, Colorectal Neoplasms
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