Multimodal Deep Learning for Grading Carpal Tunnel Syndrome: A Multicenter Study in China.

Journal: Academic radiology
Published Date: Mar 29, 2025

Abstract

RATIONALE AND OBJECTIVES: Ultrasound (US)-based deep learning (DL) models for grading the severity of carpal tunnel syndrome (CTS) are scarce. We aimed to advance CTS grading by developing a joint-DL model integrating clinical information and multimodal US features.

Authors

  • Xiaochen Shi
    Department of Trauma and Orthopedics, Peking University People's Hospital, No.11, Xizhimen South Street, Xicheng District, Beijing 100044, PR China (X.S.).
  • Tianxiang Yu
    Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
  • Yu Yuan
    Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, 430071, Hubei, China.
  • Dan Wang
    Guangdong Pharmaceutical University Guangzhou Guangdong China.
  • Jinhua Cui
    State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an 710049, China.
  • Ling Bai
  • Fang Zheng
    Center for Pharmaceutical Innovation and Research, University of Kentucky, 789 South Limestone Street, Lexington, Kentucky, 40536, USA.
  • Xiaobin Dai
    Department of Ultrasound, Qingdao Municipal Hospital, No. 5 Donghai Road, Qingdao, Shandong, 266071, PR China (F.Z., X.D.).
  • Rui Du
    Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
  • Zili Chen
    Department of Ultrasound, General Hospital of Central Theater Command, No.627, Wuluo Road, Wuchang District, Wuhan, Hubei, 430070, PR China (R.D., Z.C.).
  • Zhuhuang Zhou
    College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China.

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