Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model
Open accessAbstract
Background: In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the ‘resect and discard’ paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of ‘resect and discard’. Study design and methods: We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps. Results: The AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%. Conclusions: An AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned.
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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 [28]
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Experimental — accuracy not yet measuredCommercially available CADx systems communicate by textual or colored predictions, prediction probabilities, confidence scores or intervals, and/or position maps of suspicious areas, without justification as to why a specific method is chosen.
“Commercially available CADx systems communicate by textual or colored predictions, prediction probabilities, confidence scores or intervals, and/or position maps of suspicious areas, without justification as to why a specific method is chosen.18,19”
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 [18]
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- Authors
- Byrne, M. F., Chapados, N., Soudan, F., Oertel, C., Linares Pérez, M., Kelly, R., Iqbal, N., Chandelier, F., & Rex, D. K.
- Affiliations
- University of British Columbia (CA)
- Polytechnique Montréal (CA)
- Beaumont Hospital (IE)
- St. Luke's Hospital (IE)
- Indiana University Hospital (US)
- Corresponding author
- Byrne, Michael F
- Venue
- Gut
- Publisher
- BMJ
- Published
- 2017-10-24
- Volume
- 68
- Issue
- 1
- Pages
- 94-100
- ISSN
- 0017-5749 (ISSN-L)
- 1468-3288
- PMC
- PMC6839831
- OpenAlex
- W2765527079
- Cited by
- 635
- References
- 34
- Citations by year
- 2026: 41 · 2025: 51 · 2024: 55 · 2023: 64 · 2022: 70 · 2021: 133 · 2020: 118 · 2019: 75 · 2018: 21 · 2017: 3 · 2014: 1
- FWCI
- 26.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Gut (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- PolyPublie (École Polytechnique de Montréal) (submittedVersion)
- Funders
- ai4gi
- 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
- diminutive colorectal polyps, adenomatous polyps, hyperplastic polyps, resect and discard, computer-aided detection, convolutional neural networks, narrow-band imaging, real-time video analysis, artificial intelligence in colonoscopy, interobserver variability
- MeSH terms
- Deep Learning, Clinical Competence, Colonic Polyps, Colonoscopy, Diagnosis, Differential, Humans, Predictive Value of Tests, Sensitivity and Specificity, Video Recording, Adenomatous Polyps, Narrow Band Imaging
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