Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.

Journal Article (Journal Article)

The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate of adenomatous polyps can vary significantly among endoscopists. Here, we show that a machine-learning algorithm can detect polyps in clinical colonoscopies, in real time and with high sensitivity and specificity. We developed the deep-learning algorithm by using data from 1,290 patients, and validated it on newly collected 27,113 colonoscopy images from 1,138 patients with at least one detected polyp (per-image-sensitivity, 94.38%; per-image-specificity, 95.92%; area under the receiver operating characteristic curve, 0.984), on a public database of 612 polyp-containing images (per-image-sensitivity, 88.24%), on 138 colonoscopy videos with histologically confirmed polyps (per-image-sensitivity of 91.64%; per-polyp-sensitivity, 100%), and on 54 unaltered full-range colonoscopy videos without polyps (per-image-specificity, 95.40%). By using a multi-threaded processing system, the algorithm can process at least 25 frames per second with a latency of 76.80 ± 5.60 ms in real-time video analysis. The software may aid endoscopists while performing colonoscopies, and help assess differences in polyp and adenoma detection performance among endoscopists.

Full Text

Duke Authors

Cited Authors

  • Wang, P; Xiao, X; Glissen Brown, JR; Berzin, TM; Tu, M; Xiong, F; Hu, X; Liu, P; Song, Y; Zhang, D; Yang, X; Li, L; He, J; Yi, X; Liu, J; Liu, X

Published Date

  • October 2018

Published In

Volume / Issue

  • 2 / 10

Start / End Page

  • 741 - 748

PubMed ID

  • 31015647

Electronic International Standard Serial Number (EISSN)

  • 2157-846X

Digital Object Identifier (DOI)

  • 10.1038/s41551-018-0301-3


  • eng

Conference Location

  • England