Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial
Abstract
Background & aims One-fourth of colorectal neoplasias are missed during screening colonoscopies; these can develop into colorectal cancer (CRC). Deep learning systems allow for real-time computer-aided detection (CADe) of polyps with high accuracy. We performed a multicenter, randomized trial to assess the safety and efficacy of a CADe system in detection of colorectal neoplasias during real-time colonoscopy.Methods We analyzed data from 685 subjects (61.32 ± 10.2 years old; 337 men) undergoing screening colonoscopies for CRC, post-polypectomy surveillance, or workup due to positive results from a fecal immunochemical test or signs or symptoms of CRC, at 3 centers in Italy from September through November 2019. Patients were randomly assigned (1:1) to groups who underwent high-definition colonoscopies with the CADe system or without (controls). The CADe system included an artificial intelligence-based medical device (GI-Genius, Medtronic) trained to process colonoscopy images and superimpose them, in real time, on the endoscopy display a green box over suspected lesions. A minimum withdrawal time of 6 minutes was required. Lesions were collected and histopathology findings were used as the reference standard. The primary outcome was adenoma detection rate (ADR, the percentage of patients with at least 1 histologically proven adenoma or carcinoma). Secondary outcomes were adenomas detected per colonoscopy, non-neoplastic resection rate, and withdrawal time.Results The ADR was significantly higher in the CADe group (54.8%) than in the control group (40.4%) (relative risk [RR], 1.30; 95% confidence interval [CI], 1.14-1.45). Adenomas detected per colonoscopy were significantly higher in the CADe group (mean, 1.07 ±1.54) than in the control group (mean 0.71 ± 1.20) (incidence rate ratio, 1.46; 95% CI, 1.15-1.86). Adenomas 5 mm or smaller were detected in a significantly higher proportion of subjects in the CADe group (33.7%) than in the control group (26.5%; RR, 1.26; 95% CI, 1.01-1.52), as were adenomas of 6 to 9 mm (detected in 10.6% of subjects in the CADe group vs 5.8% in the control group; RR, 1.78; 95% CI, 1.09-2.86), regardless of morphology or location. There was no significant difference between groups in withdrawal time (417 ± 101 seconds for the CADe group vs 435 ± 149 for controls; P = .1) or proportion of subjects with resection of non-neoplastic lesions (26.0% in the CADe group vs 28.7% of controls; RR, 1.00; 95% CI, 0.90-1.12).Conclusions In a multicenter, randomized trial, we found that including CADe in real-time colonoscopy significantly increases ADR and adenomas detected per colonoscopy without increasing withdrawal time. ClinicalTrials.gov no: 04079478.
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In Repici et al. 2020, CADe improved detection of adenomas per colonoscopy, especially small and medium-sized adenomas.
“This approach improved the detection of adenomas per colonoscopy, especially small and medium-sized adenomas.”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [8]
In Repici et al. 2020, the CADe group had a significantly higher ADR (54.8%) than the control group (40.4%).
“They enrolled 685 patients and found that the CADe group had a significantly higher ADR (54.8%) compared to the control group (40.4%).”
From Advancing Colorectal Cancer Screening: A Comprehensive Systematic Review of Artificial Intelligence (AI)-Assisted Versus Routine Colonoscopy (2023) · cites this paper as [8]
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 [79]
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 [17]
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BackgroundComputer-aided detection in real-world colonoscopy: enhancing detection or offering false hope?
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- Authors
- Repici, A., Badalamenti, M., Maselli, R., Correale, L., Radaelli, F., Rondonotti, E., Ferrara, E., Spadaccini, M., Alkandari, A., Fugazza, A., Anderloni, A., Galtieri, P. A., Pellegatta, G., Carrara, S., Di Leo, M., Craviotto, V., Lamonaca, L., Lorenzetti, R., Andrealli, A., Antonelli, G., Wallace, M., Sharma, P., Rosch, T., & Hassan, C.
- Affiliations
- IRCCS Humanitas Research Hospital (IT)
- Ospedale Valduce (IT)
- Mayo Clinic in Florida (US)
- United States Department of Veterans Affairs (US)
- Corresponding author
- Repici, Alessandro
- ORCID
- Repici, Alessandro
- Badalamenti, Matteo
- Maselli, Roberta
- Correale, Loredana
- Radaelli, Franco
- Rondonotti, Emanuele
- Ferrara, Elisa
- Spadaccini, Marco
- Alkandari, Asma
- Fugazza, Alessandro
- Anderloni, Andrea
- Galtieri, Piera Alessia
- Pellegatta, Gaia
- Carrara, Silvia
- Craviotto, Vincenzo
- Lamonaca, Laura
- Lorenzetti, Roberto
- Antonelli, Giulio
- Wallace, Michael
- Sharma, Prateek
- Hassan, Cesare
- Venue
- Gastroenterology
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 159
- Issue
- 2
- Pages
- 512-520.e7
- ISSN
- 0016-5085 (ISSN-L)
- OpenAlex
- W3021812344
- Cited by
- 646
- References
- 21
- Citations by year
- 2026: 101 · 2025: 102 · 2024: 84 · 2023: 113 · 2022: 121 · 2021: 92 · 2020: 31 · 2017: 1
- FWCI
- 32.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Gastroenterology (publishedVersion)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- 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 neoplasia, colorectal adenoma detection, colorectal cancer screening, colonoscopy, deep learning, GI Genius, small adenomas, polyp detection, withdrawal time, post-polypectomy surveillance, artificial intelligence in endoscopy, missed lesions, fecal immunochemical test
- MeSH terms
- Deep Learning, Adenoma, Aged, Biopsy, Colon, Colonoscopy, Female, Humans, Image Interpretation, Computer-Assisted, Male, Mass Screening, Middle Aged, Time Factors, Colorectal Neoplasms
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