Artificial Intelligence Allows Leaving-In-Situ Colorectal Polyps
Open accessAbstract
BACKGROUND & AIMS: Artificial Intelligence (AI) could support cost-saving strategies for colonoscopy because of its accuracy in the optical diagnosis of colorectal polyps. However, AI must meet predefined criteria to be implemented in clinical settings. METHODS: An approved computer-aided diagnosis (CADx) module for differentiating between adenoma and nonadenoma in unmagnified white-light colonoscopy was used in a consecutive series of colonoscopies. For each polyp, CADx output and subsequent endoscopist diagnosis with advanced imaging were matched against the histology gold standard. The primary outcome was the negative predictive value (NPV) of CADx for adenomatous histology for ≤5-mm rectosigmoid lesions. We also calculated the NPV for AI-assisted endoscopist predictions, and agreement between CADx and histology-based postpolypectomy surveillance intervals according to European and American guidelines. RESULTS: Overall, 544 polyps were removed in 162 patients, of which 295 (54.2%) were ≤5-mm rectosigmoid histologically verified lesions. CADx diagnosis was feasible in 291 of 295 (98.6%), and the NPV for ≤5-mm rectosigmoid lesions was 97.6% (95% CI, 94.1%-99.1%). There were 242 of 295 (82%) lesions that were amenable for a leave-in-situ strategy. Based on CADx output, 212 of 544 (39%) would be amenable to a resect-and-discard strategy, resulting in a 95.6% (95% CI, 90.8%-98.0%) and 95.9% (95% CI, 89.8%-98.4%) agreement between CADx- and histology-based surveillance intervals according to European and American guidelines, respectively. A similar NPV (97.6%; 95% CI, 94.8%-99.1%) for ≤5-mm rectosigmoids was achieved by AI-assisted endoscopists assessing polyps with electronic chromoendoscopy, with a CADx-concordant diagnosis in 97.2% of cases. CONCLUSIONS: In this study, CADx without advanced imaging exceeded the benchmarks required for optical diagnosis of colorectal polyps. CADx could help implement cost-saving strategies in colonoscopy by reducing the burden of polypectomy and/or pathology. CLINICALTRIALS: gov registration number: NCT04884581.
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Previous studies on CADx systems for colonoscopy focused on assessing the performances of AI against the accuracy of physicians or against criteria established by gastroenterology societies for implementing the technology in a clinical setting.
“Previous studies on cadx systems for colonoscopy focused on assessing the performances of ai against the accuracy of physicians19,20,27–30, or against the criteria established by gastroenterology societies for implementing the technology in a clinical setting31–34.”
From Experimental evidence of effective human–AI collaboration in medical decision-making (2022) · cites this paper as [34]
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- Authors
- Hassan, C., Balsamo, G., Lorenzetti, R., Zullo, A., & Antonelli, G.
- Affiliations
- Humanitas University (IT)
- ASL Roma (IT)
- Sapienza University of Rome (IT)
- Corresponding author
- Hassan, Cesare
- Venue
- Clinical Gastroenterology and Hepatology
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 20
- Issue
- 11
- Pages
- 2505-2513.e4
- ISSN
- 1542-3565 (ISSN-L)
- OpenAlex
- W4285014966
- Cited by
- 119
- References
- 34
- Citations by year
- 2026: 12 · 2025: 28 · 2024: 39 · 2023: 35 · 2022: 5
- FWCI
- 11.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Gastroenterology and Hepatology (publishedVersion, cc-by-nc-nd)
- 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 › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, computer-aided diagnosis, colorectal polyps, adenoma, rectosigmoid polyps, negative predictive value, post-polypectomy surveillance, white light colonoscopy, histopathological correlation, advanced imaging
- MeSH terms
- Adenoma, Artificial Intelligence, Colonic Polyps, Colonoscopy, Humans, Colorectal Neoplasms, Narrow Band Imaging
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