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

Nature Biomedical Engineeringvolume 2pages741748 (2018) | Download Citation

Abstract

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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Data availability

The authors declare that all data supporting the findings of this study are available within the paper and its supplementary information. Restrictions apply to the availability of the medical training/ validation data, which were used with permission for the current study, and so are not publicly available. Some data may be available from the authors upon reasonable request and with permission of the Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Acknowledgements

We thank the Machine Intelligence Laboratory of the University of Cambridge for developing SegNet and making it publicly available.

Author information

Affiliations

  1. Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China

    • Pu Wang
    • , Mengtian Tu
    • , Fei Xiong
    • , Xiao Hu
    • , Peixi Liu
    • , Yan Song
    • , Di Zhang
    • , Xue Yang
    • , Liangping Li
    •  & Xiaogang Liu
  2. Shanghai Wision AI Co., Ltd, Shanghai, China

    • Xiao Xiao
    • , Jiong He
    • , Xin Yi
    •  & Jingjia Liu
  3. Beth Israel Deaconess Medical Center and Harvard Medical School, Center for Advanced Endoscopy, Boston , MA, USA

    • Jeremy R. Glissen Brown
    •  & Tyler M. Berzin

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Contributions

P.W. and X.L. contributed to study concept and design. P.W., X.L., L.L., M.T., F.X., X.H., P.L., Y.S., D.Z. and X.Yang. contributed to acquisition of data and statistical analysis. P.W., T.M.B. and J.R.G.B contributed to analysis, interpretation of data and drafting of the manuscript. X.X. contributed to algorithm development. J.L., J.H. and X.Yi. contributed to algorithm and software/hardware implementation. All authors read and approved the final manuscript.

Competing interests

X.X., J.L., J.H. and X.Y. are employees of Shanghai Wision AI Co., Ltd. The automatic polyp detection system was developed by the company and the software was provided free of charge for the purposes of this study. All other authors declare no competing interests.

Corresponding author

Correspondence to Xiaogang Liu.

Supplementary information

  1. Supplementary Information

    Supplementary figures and video captions.

  2. Reporting Summary

  3. Supplementary Video 1

    Real-time visual assistance during colonoscopy on an adjacent monitor.

  4. Supplementary Video 2

    Additional video of real-time visual assistance during colonoscopy on an adjacent monitor.

  5. Supplementary Video 3

    Sample video from the simulated real-time video analysis.

  6. Supplementary Video 4

    Additional sample video from the simulated real-time video analysis.

  7. Supplementary Video 5

    Demonstration of simulated real-time video analysis on datasets C and D.

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DOI

https://doi.org/10.1038/s41551-018-0301-3

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