Computer-Aided Diagnosis of Duchenne Muscular Dystrophy Based on Texture Pattern Recognition on Ultrasound Images Using Unsupervised Clustering Algorithms and Deep Learning.

Journal: Ultrasound in medicine & biology
PMID: 38637169

Abstract

OBJECTIVE: The feasibility of using deep learning in ultrasound imaging to predict the ambulatory status of patients with Duchenne muscular dystrophy (DMD) was previously explored for the first time. The present study further used clustering algorithms for the texture reconstruction of ultrasound images of DMD data sets and analyzed the difference in echo intensity between disease stages.

Authors

  • Ai-Ho Liao
    Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan; Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan. Electronic address: aiho@mail.ntust.edu.tw.
  • Chih-Hung Wang
    National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan.
  • Chong-Yu Wang
    Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Hao-Li Liu
    Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
  • Ho-Chiao Chuang
    Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan.
  • Wei-Jye Tseng
    Graduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
  • Wen-Chin Weng
    Department of Pediatrics, National Taiwan University Hospital, and College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Pediatric Neurology, National Taiwan University Children's Hospital, Taipei, Taiwan.
  • Cheng-Ping Shih
    Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
  • Po-Hsiang Tsui
    Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan.

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