Impact of artificial intelligence on miss rate of colorectal neoplasia
Open accessAbstract
Background & aims Artificial intelligence (AI) may detect colorectal polyps that have been missed due to perceptual pitfalls. By reducing such miss rate, AI may increase the detection of colorectal neoplasia leading to a higher degree of colorectal cancer (CRC) prevention. Methods Patients undergoing CRC screening or surveillance were enrolled in 8 centers (Italy, UK, US), and randomized (1:1) to undergo 2 same-day, back-to-back colonoscopies with or without AI (deep learning computer aided diagnosis endoscopy) in 2 different arms, namely AI followed by colonoscopy without AI or vice-versa. Adenoma miss rate (AMR) was calculated as the number of histologically verified lesions detected at second colonoscopy divided by the total number of lesions detected at first and second colonoscopy. Mean number of lesions detected in the second colonoscopy and proportion of false negative subjects (no lesion at first colonoscopy and at least 1 at second) were calculated. Odds ratios (ORs) and 95% confidence intervals (CIs) were adjusted by endoscopist, age, sex, and indication for colonoscopy. Adverse events were also measured. Results A total of 230 subjects (116 AI first, 114 standard colonoscopy first) were included in the study analysis. AMR was 15.5% (38 of 246) and 32.4% (80 of 247) in the arm with AI and non-AI colonoscopy first, respectively (adjusted OR, 0.38; 95% CI, 0.23-0.62). In detail, AMR was lower for AI first for the ≤5 mm (15.9% vs 35.8%; OR, 0.34; 95% CI, 0.21-0.55) and…
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Larger (>10 mm) or polypoid adenomas also benefit from CADe assistance, albeit possibly to a lesser extent.
“Nevertheless, larger ( > 10 mm) or polypoid adenomas, which are more apparent, also benefit from CADe assistance, albeit possibly to a lesser extent [76 , 84] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [84]
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Self-citation
- Authors
- Wallace, M. B., Sharma, P., Bhandari, P., East, J., Antonelli, G., Lorenzetti, R., Vieth, M., Speranza, I., Spadaccini, M., Desai, M., Lukens, F. J., Babameto, G., Batista, D., Singh, D., Palmer, W., Ramirez, F., Palmer, R., Lunsford, T., Ruff, K., Bird-Liebermann, E., Ciofoaia, V., Arndtz, S., Cangemi, D., Puddick, K., Derfus, G., Johal, A. S., Barawi, M., Longo, L., Moro, L., Repici, A., & Hassan, C.
- Affiliations
- Mayo Clinic (US)
- University of Kansas Medical Center (US)
- Queen Alexandra Hospital (GB)
- John Radcliffe Hospital (GB)
- Sapienza University of Rome (IT)
- Bayreuth Medical Center (DE)
- Mayo Clinic in Arizona (US)
- Geisinger Medical Center (US)
- St. John Hospital & Medical Center (US)
- Humanitas University (IT)
- Corresponding author
- Wallace, Michael B.
- ORCID
- Venue
- Gastroenterology
- Publisher
- Elsevier BV
- Published
- 2022-07
- Volume
- 163
- Issue
- 1
- Pages
- 295-304.e5
- ISSN
- 0016-5085 (ISSN-L)
- OpenAlex
- W4221018164
- Cited by
- 273
- References
- 2
- Citations by year
- 2026: 47 · 2025: 68 · 2024: 77 · 2023: 61 · 2022: 20
- FWCI
- 23.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Gastroenterology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Funders
- Medtronic Inc
- 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
- artificial intelligence, colorectal neoplasia, adenoma miss rate, computer-aided diagnosis, colonoscopy, colorectal cancer screening, colorectal cancer surveillance, colorectal polyps, small polyps, colorectal cancer prevention, deep learning, adverse events, patient satisfaction, cost-effectiveness
- MeSH terms
- Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Humans, Colorectal Neoplasms, Early Detection of Cancer
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