Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study
Abstract
Background Optical diagnosis of colonic polyps is poorly reproducible outside of high volume referral centers. The present study aimed to assess whether real-time artificial intelligence (AI)-assisted optical diagnosis is accurate enough to implement the leave-in-situ strategy for diminutive (≤ 5 mm) rectosigmoid polyps (DRSPs). Methods Consecutive colonoscopy outpatients with ≥ 1 DRSP were included. DRSPs were categorized as adenomas or nonadenomas by the endoscopists, who had differing expertise in optical diagnosis, with the assistance of a real-time AI system (CAD-EYE). The primary end point was ≥ 90 % negative predictive value (NPV) for adenomatous histology in high confidence AI-assisted optical diagnosis of DRSPs (Preservation and Incorporation of Valuable endoscopic Innovations [PIVI-1] threshold), with histopathology as the reference standard. The agreement between optical- and histology-based post-polypectomy surveillance intervals (≥ 90 %; PIVI-2 threshold) was also calculated according to European Society of Gastrointestinal Endoscopy (ESGE) and United States Multi-Society Task Force (USMSTF) guidelines. Results Overall 596 DRSPs were retrieved for histology in 389 patients; an AI-assisted high confidence optical diagnosis was made in 92.3 %. The NPV of AI-assisted optical diagnosis for DRSPs (PIVI-1) was 91.0 % (95 %CI 87.1 %–93.9 %). The PIVI-2 threshold was met with 97.4 % (95 %CI 95.7 %–98.9 %) and 92.6 % (95 %CI 90.0 %–95.2 %) of patients according to ESGE and USMSTF, respectively. AI-assisted optical diagnosis accuracy was significantly lower for nonexperts (82.3 %, 95 %CI 76.4 %–87.3 %) than for experts (91.9 %, 95 %CI 88.5 %–94.5 %); however, nonexperts quickly approached the performance levels of experts over time. Conclusion AI-assisted optical diagnosis matches the required PIVI thresholds. This does not however offset the need for endoscopistsʼ high level confidence and expertise. The AI system seems to be useful, especially for nonexperts.
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No harmful effects occurred even when AI performance was inferior to human experts.
“and no harmful effects occurred even when AI performance was inferior to human experts [95 , 96] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [96]
Experienced endoscopists' optical diagnosis performance did not significantly benefit from AI assistance.
“Results show that experienced endoscopists' optical diagnosis performance did not significantly benefit from AI assistance”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [96]
Performance of available CADx systems is comparable, if not inferior, to that of experienced endoscopists.
“Furthermore, the performance of available CADx systems is comparable, if not inferior, to that of experienced endoscopists [66 , 94-96] .”
From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [96]
Integration of CADx into clinical practice has not consistently enhanced the diagnostic accuracy of endoscopists.
“However, their integration into clinical practice has not consistently enhanced the diagnostic accuracy of endoscopists.14-16”
From Structured Integration of an Artificial Intelligence-Based System for the Optical Diagnosis of Colorectal Polyps (2026) · cites this paper as [16]
A previous study showed higher levels of CADx-assisted diagnostic performance compared to performances of endoscopists alone.
“This result is in line with a previous study that showed higher levels of CADx-assisted diagnostic performance compared to performances of endoscopists alone.24”
From Appropriate trust in artificial intelligence for the optical diagnosis of colorectal polyps: the role of human/artificial intelligence interaction (2024) · cites this paper as [24]
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Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?
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- Authors
- Rondonotti, E., Hassan, C., Tamanini, G., Antonelli, G., Andrisani, G., Leonetti, G., Paggi, S., Amato, A., Scardino, G., Di Paolo, D., Mandelli, G., Lenoci, N., Terreni, N., Andrealli, A., Maselli, R., Spadaccini, M., Galtieri, P. A., Correale, L., Repici, A., Di Matteo, F. M., Ambrosiani, L., Filippi, E., Sharma, P., & Radaelli, F.
- Affiliations
- Ospedale Valduce (IT)
- Digestive Endoscopy Unit, Nuovo Regina Margherita Hospital, Rome, Italy
- Sapienza University of Rome (IT)
- Università Campus Bio-Medico (IT)
- Gastroenterology Unit, Valduce Hospital, Como, Italy
- Humanitas University (IT)
- IRCCS Humanitas Research Hospital (IT)
- Digestive Endoscopy Unit, Campus Bio-Medico, University of Rome, Rome, Italy
- University of Kansas Medical Center (US)
- ORCID
- Venue
- Endoscopy
- Publisher
- Georg Thieme Verlag KG
- Published
- 2022-05-13
- Volume
- 55
- Issue
- 01
- Pages
- 14-22
- ISSN
- 0013-726X (ISSN-L)
- 1438-8812
- OpenAlex
- W4280489940
- Cited by
- 128
- References
- 4
- Citations by year
- 2026: 18 · 2025: 40 · 2024: 34 · 2023: 31 · 2022: 5
- FWCI
- 11.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Endoscopy (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
- resect and discard strategy, negative predictive value, post-polypectomy surveillance, endoscopist expertise, colonoscopy, colorectal adenoma, histopathological correlation, endoscopic submucosal dissection
- MeSH terms
- Adenoma, Artificial Intelligence, Colon, Colonic Polyps, Colonoscopy, Humans, Colorectal Neoplasms, Narrow Band Imaging
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