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

Abstract

backgroundIn 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. IntroductIonEndoscopists combine their knowledge of the spectrum of endoscopic appearances of precancerous lesions with meticulous mechanical exploration and cleaning of mucosal surfaces to maximise lesion detection during colonoscopy. An extension of detection is endoscopic prediction of lesion histology, including differentiation of precancerous lesions from non-neoplastic lesions, and prediction of deep submucosal invasion of cancer.1 2 Image analysis can guide whether lesion removal is necessary and direct an endoscopist to the best resection method. 1-3Image analysis during colonoscopy has achieved increasing acceptance as a means to accurately predict the histology of diminutive lesions, 4 5 which have minimal risk of cancer, 6 so that these diminutive lesions could be resected and discarded without pathological assessment or left in place without resection in the case of diminutive distal colon hyperplastic polyps.3 Discarding most diminutive lesions without pathological assessment has the potential for large cost saving with minimal risk.

Artificial intelligence and colonoscopy: Current status and future perspectives

“…The AI model did not generate sufficient confidence to predict the histology of 15% of the polyps. For the remaining 106 diminutive polyps, however, the sensitivity for identifying adenomas was 98%, specificity 83%, NPV 97%, and positive predictive value (PPV) 90% . Similarly, Chen et al .…”

“…Deep learning‐based algorithm designed for magnified narrow‐band imaging (near‐focus mode) identifies a small polyp as type 2 in the Narrow‐band imaging International Colorectal Endoscopic (NICE) classification (indicative of neoplastic change) with a probability of 98%. (Image courtesy of Byrne et al …”

Potential of artificial intelligence‐assisted colonoscopy using an endocytoscope (with video)

“…However, a substantial number of unnecessary polypectomies are carried out for non‐neoplastic polyps because of endoscopists’ misdiagnoses, resulting in considerable financial concerns . Thus, increasing attention is being given to artificial intelligence (AI) designed to assist accurate optical biopsy of colorectal polyps …”

Human-machine collaboration: bringing artificial intelligence into colonoscopy

“…CAD offers a promising timely solution to overcome this barrier by providing decision support. Byrne et al used a CNN to differentiate diminutive adenomas from hyperplastic polyps on unaltered narrow band imaging (NBI) videos collected from a previous study using standard colonoscopy 11. The algorithm provided an associated probability score for predictions.…”