IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-31449-3.html
   My bibliography  Save this article

Deep learning to diagnose Hashimoto’s thyroiditis from sonographic images

Author

Listed:
  • Qiang Zhang

    (Tianjin Medical University)

  • Sheng Zhang

    (Tianjin Medical University)

  • Yi Pan

    (Tianjin Medical University)

  • Lin Sun

    (Tianjin Medical University)

  • Jianxin Li

    (Cheeloo College of Medicine)

  • Yu Qiao

    (Tianjin Medical University)

  • Jing Zhao

    (Tianjin Medical University)

  • Xiaoqing Wang

    (Tianjin Medical University)

  • Yixing Feng

    (Tianjin Medical University)

  • Yanhui Zhao

    (Affiliated Hospital of Chifeng University)

  • Zhiming Zheng

    (Integrated Traditional Chinese and Western Medicine Hospital of Jilin City Jilin Province)

  • Xiangming Yang

    (Dezhou Municipal Hospital)

  • Lixia Liu

    (Affiliated Hospital of Hebei University)

  • Chunxin Qin

    (Weihai Municipal Hospital, Cheeloo College of Medicine)

  • Ke Zhao

    (Tianjin Medical University General Hospital)

  • Xiaonan Liu

    (Tianjin 4th Centre Hospital)

  • Caixia Li

    (Tianjin 4th Centre Hospital)

  • Liuyang Zhang

    (Affiliated Hospital of Chengde Medical University)

  • Chunrui Yang

    (The Second Hospital of Tianjin Medical University)

  • Na Zhuo

    (The Second Hospital of Tianjin Medical University)

  • Hong Zhang

    (The Second Hospital of Jilin University)

  • Jie Liu

    (Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine of Hebei Province)

  • Jinglei Gao

    (People’s Hospital of Dingzhou)

  • Xiaoling Di

    (People’s Hospital of Dingzhou)

  • Fanbo Meng

    (Affiliated Hospital of Shaoxing University)

  • Linlei Zhang

    (The Second Hospital of Hebei Medical University)

  • Yuxuan Wang

    (Tianjin Medical University)

  • Yuansheng Duan

    (Tianjin Medical University)

  • Hongru Shen

    (Tianjin Medical University)

  • Yang Li

    (Tianjin Medical University)

  • Meng Yang

    (Tianjin Medical University)

  • Yichen Yang

    (Tianjin Medical University)

  • Xiaojie Xin

    (Tianjin Medical University)

  • Xi Wei

    (Tianjin Medical University)

  • Xuan Zhou

    (Tianjin Medical University)

  • Rui Jin

    (Tianjin Medical University)

  • Lun Zhang

    (Tianjin Medical University)

  • Xudong Wang

    (Tianjin Medical University)

  • Fengju Song

    (Tianjin Medical University)

  • Xiangqian Zheng

    (Tianjin Medical University)

  • Ming Gao

    (Tianjin Medical University
    Tianjin Union Medical Center)

  • Kexin Chen

    (Tianjin Medical University)

  • Xiangchun Li

    (Tianjin Medical University)

Abstract

Hashimoto’s thyroiditis (HT) is the main cause of hypothyroidism. We develop a deep learning model called HTNet for diagnosis of HT by training on 106,513 thyroid ultrasound images from 17,934 patients and test its performance on 5051 patients from 2 datasets of static images and 1 dataset of video data. HTNet achieves an area under the receiver operating curve (AUC) of 0.905 (95% CI: 0.894 to 0.915), 0.888 (0.836–0.939) and 0.895 (0.862–0.927). HTNet exceeds radiologists’ performance on accuracy (83.2% versus 79.8%; binomial test, p

Suggested Citation

  • Qiang Zhang & Sheng Zhang & Yi Pan & Lin Sun & Jianxin Li & Yu Qiao & Jing Zhao & Xiaoqing Wang & Yixing Feng & Yanhui Zhao & Zhiming Zheng & Xiangming Yang & Lixia Liu & Chunxin Qin & Ke Zhao & Xiaon, 2022. "Deep learning to diagnose Hashimoto’s thyroiditis from sonographic images," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-31449-3
    DOI: 10.1038/s41467-022-31449-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-31449-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-31449-3?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kang Su & Jingwei Liu & Xiaoqi Ren & Yingxiang Huo & Guanglong Du & Wei Zhao & Xueqian Wang & Bin Liang & Di Li & Peter Xiaoping Liu, 2024. "A fully autonomous robotic ultrasound system for thyroid scanning," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

    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:13:y:2022:i:1:d:10.1038_s41467-022-31449-3. 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.