Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model
By M. Byrne, Nicolas Chapados, F. Soudan, Clemens Oertel, Milagros Linares Pérez, Raymond Kelly, Nadeem Iqbal, F. Chandelier, Douglas Rex. Gut, 2017
557 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Oncology, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/0fa536f28d4fd1648720a5f4670d54f6f1a8deb5
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In Artificial Intelligence
- Alexey A. Shvets
- Alexandr A Kalinin
- Alexander Rakhlin
- Vladimir I. Iglovikov
- Pål Halvorsen
- Kensaku Mori
- Ohad Shamir
- Sebastian U. Stich
In Oncology
In Pulmonary and Respiratory Medicine
Citations in the corpus
- On evaluation metrics for medical applications of artificial intelligence (2021, background): “In Study 3 , Byrne et al. 18 remove data samples for which the classifier does not achieve high confidence, as well as videos with more than one polyp.”
- Angiodysplasia Detection and Localization Using Deep Convolutional Neural Networks (2018, background): “In the analysis of video capsule endoscopy, deep learning has recently demostrated promising results for polyp detection (Tajbakhsh et al., 2015; Yuan and Meng, 2017; Byrne et al., 2017; Murthy et al., 2017).”
- Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy (2018, background): “Recently, Byrne and colleagues (6) developed an excellent CAD model with an NPV greater than 90% for diminutive adenomas by applying deep learning technology to near-focus narrowband images.”