IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-42451-8.html
   My bibliography  Save this article

Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study

Author

Listed:
  • Jianfeng Cao

    (The Chinese University of Hong Kong)

  • Hon-Chi Yip

    (The Chinese University of Hong Kong)

  • Yueyao Chen

    (The Chinese University of Hong Kong)

  • Markus Scheppach

    (University Hospital of Augsburg)

  • Xiaobei Luo

    (Southern Medical University)

  • Hongzheng Yang

    (The Chinese University of Hong Kong)

  • Ming Kit Cheng

    (The Chinese University of Hong Kong)

  • Yonghao Long

    (The Chinese University of Hong Kong)

  • Yueming Jin

    (National University of Singapore)

  • Philip Wai-Yan Chiu

    (Multi-scale Medical Robotics Center and The Chinese University of Hong Kong)

  • Yeung Yam

    (The Chinese University of Hong Kong
    Multi-scale Medical Robotics Center and The Chinese University of Hong Kong
    Centre for Perceptual and Interactive Intelligence and The Chinese University of Hong Kong)

  • Helen Mei-Ling Meng

    (Centre for Perceptual and Interactive Intelligence and The Chinese University of Hong Kong)

  • Qi Dou

    (The Chinese University of Hong Kong)

Abstract

Recent advancements in artificial intelligence have witnessed human-level performance; however, AI-enabled cognitive assistance for therapeutic procedures has not been fully explored nor pre-clinically validated. Here we propose AI-Endo, an intelligent surgical workflow recognition suit, for endoscopic submucosal dissection (ESD). Our AI-Endo is trained on high-quality ESD cases from an expert endoscopist, covering a decade time expansion and consisting of 201,026 labeled frames. The learned model demonstrates outstanding performance on validation data, including cases from relatively junior endoscopists with various skill levels, procedures conducted with different endoscopy systems and therapeutic skills, and cohorts from international multi-centers. Furthermore, we integrate our AI-Endo with the Olympus endoscopic system and validate the AI-enabled cognitive assistance system with animal studies in live ESD training sessions. Dedicated data analysis from surgical phase recognition results is summarized in an automatically generated report for skill assessment.

Suggested Citation

  • Jianfeng Cao & Hon-Chi Yip & Yueyao Chen & Markus Scheppach & Xiaobei Luo & Hongzheng Yang & Ming Kit Cheng & Yonghao Long & Yueming Jin & Philip Wai-Yan Chiu & Yeung Yam & Helen Mei-Ling Meng & Qi Do, 2023. "Intelligent surgical workflow recognition for endoscopic submucosal dissection with real-time animal study," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42451-8
    DOI: 10.1038/s41467-023-42451-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-42451-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-42451-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42451-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.