Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy

By Gregor Urban, Priya Tripathi, T. Alkayali, Mohit Mittal, F. Jalali, W. Karnes, Pierre Baldi. Gastroenterology, 2018

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Background & Aims: The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect adenoma prevalence rate, estimated to be greater than 50% among the screening-age population. Yet the rate of adenoma detection by colonoscopists varies from 7% to 53%. It is estimated that every 1% increase in ADR reduces the risk of interval colorectal cancers by 3-6%. New strategies are needed to increase the ADR during colonoscopy. We tested the ability of computer-assisted image analysis, with convolutional neural networks (a deep learning model for image analysis), to improve polyp detection, a surrogate of ADR. Methods: We designed and trained deep convolutional neural networks (CNN) to detect polyps using a diverse and representative set of 8641 hand labeled images from screening colonoscopies collected from over 2000 patients. We tested the models on 20 colonoscopy videos with a total duration of 5 hours. Expert colonoscopists were asked to identify all polyps in 9 de-identified colonoscopy videos, selected from archived video studies, either with or without benefit of the CNN overlay. Their findings were compared with those of the CNN, using CNN-assisted expert review as the reference. Results: When tested on manually labeled images, the CNN identified polyps with an area under the receiver operating characteristic curve (ROC-AUC) of 0.991 and an accuracy of 96.4%. In the analysis of colonoscopy videos in which 28 polyps were removed, 4 expert reviewers identified 8 additional polyps without CNN assistance that had not been removed and identified an additional 17 polyps with CNN assistance (45 in total). All polyps removed and identified by expert review were detected by the CNN. The CNN had a false-positive rate of 7%. Conclusion: In a set of 8641 colonoscopy images containing 4088 unique polyps the CNN identified polyps with a cross-validation accuracy of 96.4% and ROC-AUC value of 0.991. The CNN system can detect and localize polyps well within real-time constraints using an ordinary desktop machine with a contemporary graphics processing unit. This system could increase ADR and reduce interval colorectal cancers but requires validation in large multicenter trials.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a0f9f4eae59cead30fd698d03acd868a7a94b3bf