Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model

2017/10/24 by Michael F. Byrne, Michael F Byrne, Nicolas Chapados +9 · 636 citations
Computer Science · Medicine · #AI in cancer detection #Artificial intelligence #Cancer #Colonoscopy #Colorectal Cancer Screening and Detection #Colorectal Polyp #Colorectal cancer #Computer science #Diminutive #Endoscopy #Gastric Cancer Management and Outcomes #Gastroenterology #Hyperplastic Polyp #Internal medicine #Medicine #Narrow-band imaging #Predictive value #Radiology

paper · pdf · doi:10.1136/gutjnl-2017-314547

published in Gut 68(1), 94-100 (BMJ)

openalex publication_date 2017/10/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

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.

Citations

• Serrated Lesions of the Colorectum: Review and Recommendations From an Expert Panel
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