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Original article
Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model
  1. Michael F Byrne1,
  2. Nicolas Chapados2,3,
  3. Florian Soudan2,
  4. Clemens Oertel2,
  5. Milagros Linares Pérez4,
  6. Raymond Kelly5,
  7. Nadeem Iqbal6,
  8. Florent Chandelier2,
  9. Douglas K Rex7
  1. 1 Division of Gastroenterology, Vancouver General Hospital, Vancouver, British Columbia, Canada
  2. 2 Department of Technology, Imagia, Montreal, Quebec, Canada
  3. 3 Department of Applied Mathematics, Ecole Polytechnique de Montreal, Montreal, Quebec, Canada
  4. 4 Department of Gastroenterology, Universidad de Buenos Aires, Buenos Aires, Argentina
  5. 5 Department of Anaesthetics, Beaumont Hospital, Dublin, Ireland
  6. 6 Department of Gastroenterology, Saint Luke’s Hospital, Kilkenny, Ireland
  7. 7 Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, Indiana, USA
  1. Correspondence to Dr Michael F Byrne, Division of Gastroenterology, Vancouver General Hospital, University of British Columbia, Vancouver, BC V5Z 1M9, Canada; mike{at}


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.

  • polyp
  • colorectal adenomas
  • endoscopic polypectomy

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  • Contributors Study conception and design: MFB. Drafting the manuscript: MFB and DKR. Data analysis: all authors. Development of the artificial intelligence model: NC, FS and FC. Video recording: DKR. Critical revision of the manuscript: all authors.

  • Funding This work was primarily supported by ’ai4gi', a joint venture between Satis Operations Inc and Imagia Cybernetics.

  • Competing interests MFB: CEO and shareholder, Satis Operations Inc, ’ai4gi’ joint venture; research support: Boston Scientific. NC: Imagia shareholder, ‘ai4gi’ joint venture. FS: Imagia shareholder, ‘ai4gi’ joint venture. CO: Imagia shareholder, ‘ai4gi' joint venture. FC: Imagia shareholder, ’ai4gi' joint venture. DKR: consultant: Olympus Corp and Boston Scientific; research support: Boston Scientific, Endochoice and EndoAid.

  • Ethics approval Indiana University.

  • Provenance and peer review Not commissioned; internally peer reviewed.

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