Machine learning-based radiomics using MRI to differentiate early-stage Duchenne and Becker muscular dystrophy in children.

Journal: BMC musculoskeletal disorders
PMID: 40121488

Abstract

OBJECTIVES: Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) present similar symptoms in the early stage, complicating their differentiation. This study aims to develop a classification model using radiomic features from MRI T2-weighted Dixon sequences to increase the accuracy of distinguishing DMD and BMD in the early disease stage.

Authors

  • Taiya Chen
    Department of Radiology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, Shenzhen, China.
  • Haoran Zhu
    Center for Integrated Research Computing, University of Rochester, Rochester, New York 14627, United States.
  • Yingyi Hu
    Department of Radiology, Shenzhen Children's Hospital, China Medical University, Shenzhen, China.
  • Yang Huang
    School of Computer and Electronic Information, Nanjing Normal University, Nanjing 210023, China.
  • Wengan He
    Shenzhen ZhenData Intelligent Technology Co., Ltd, Shenzhen, China.
  • Yizhen Luo
    Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China.
  • Zeqi Wu
    Department of Radiology, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, People's Republic of China.
  • Diangang Fang
    Department of Radiology, Shenzhen Children's Hospital, Shantou University Medical College, Shenzhen, China.
  • Longwei Sun
    Department of Radiology, Shenzhen Children's Hospital, Guangdong, 518034, China.
  • Hongwu Zeng
    Department of Radiology, Shenzhen Children's Hospital, Shenzhen, Guangdong, People's Republic of China. homerzeng@126.com.
  • Zhiyong Li
    School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.

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