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

ABSTRACT:

Background & aims

The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the adenoma prevalence rate, which is estimated to be higher than 50% in the screening-age population. However, the ADR by colonoscopists varies from 7% to 53%. It is estimated that every 1% increase in ADR lowers 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 using convolutional neural networks (CNNs; a deep learning model for image analysis) to improve polyp detection, a surrogate of ADR.

Methods

We designed and trained deep CNNs to detect polyps using a diverse and representative set of 8,641 hand-labele

SUBMITTER: Urban G 

PROVIDER: S-EPMC6174102 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature