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

Publication ,  Journal Article
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 in: Nat Biomed Eng
October 2018

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.

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Published In

Nat Biomed Eng

DOI

EISSN

2157-846X

Publication Date

October 2018

Volume

2

Issue

10

Start / End Page

741 / 748

Location

England

Related Subject Headings

  • Software
  • ROC Curve
  • Precancerous Conditions
  • Image Interpretation, Computer-Assisted
  • Humans
  • Deep Learning
  • Databases, Factual
  • Colonoscopy
  • Colonic Polyps
  • Colonic Neoplasms
 

Citation

APA
Chicago
ICMJE
MLA
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Wang, P., Xiao, X., Glissen Brown, J. R., Berzin, T. M., Tu, M., Xiong, F., … Liu, X. (2018). Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng, 2(10), 741–748. https://doi.org/10.1038/s41551-018-0301-3
Wang, Pu, Xiao Xiao, Jeremy R. Glissen Brown, Tyler M. Berzin, Mengtian Tu, Fei Xiong, Xiao Hu, et al. “Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.Nat Biomed Eng 2, no. 10 (October 2018): 741–48. https://doi.org/10.1038/s41551-018-0301-3.
Wang P, Xiao X, Glissen Brown JR, Berzin TM, Tu M, Xiong F, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2018 Oct;2(10):741–8.
Wang, Pu, et al. “Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.Nat Biomed Eng, vol. 2, no. 10, Oct. 2018, pp. 741–48. Pubmed, doi:10.1038/s41551-018-0301-3.
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. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng. 2018 Oct;2(10):741–748.

Published In

Nat Biomed Eng

DOI

EISSN

2157-846X

Publication Date

October 2018

Volume

2

Issue

10

Start / End Page

741 / 748

Location

England

Related Subject Headings

  • Software
  • ROC Curve
  • Precancerous Conditions
  • Image Interpretation, Computer-Assisted
  • Humans
  • Deep Learning
  • Databases, Factual
  • Colonoscopy
  • Colonic Polyps
  • Colonic Neoplasms