One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning.

Journal: Ultrasound in medicine & biology
PMID: 38044200

Abstract

OBJECTIVE: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS), although identification of the median nerve (MN) and diagnosis of CTS depend heavily on the expertise of examiners. In the aim of alleviating this problem, we developed a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluated its effectiveness as a computer-aided diagnostic tool.

Authors

  • Jiayu Peng
    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Jiajun Zeng
    Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China; Medical Ultrasound Image Computing (MUSIC) Lab, Shenzhen University, Shenzhen, China; Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China.
  • Manlin Lai
    Ultrasound Division, Department of Medical Imaging, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
  • Ruobing Huang
    Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, UK. Electronic address: ruobing.huang@eng.ox.ac.uk.
  • Dong Ni
  • Zhenzhou Li
    Department of Ultrasound, Department of Ultrasound, The Second People's Hospital of Shenzhen, The First Affiliated Hospital of Shenzhen University, Shenzhen, 518060, China.

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