HarDNet-based deep learning model for osteoporosis screening and bone mineral density inference from hand radiographs.

Journal: Bone
PMID: 39500404

Abstract

PURPOSE: Osteoporosis, affecting over 200 million individuals, often remains unrecognized and untreated, increasing the risk of fractures in older adults. Osteoporosis is typically diagnosed with bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA). This study aims to develop DeepDXA-Hand, a deep learning model using the efficient CNN-based deep learning architecture, for opportunistic osteoporosis screening from hand radiographs.

Authors

  • Chan-Shien Ho
    Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan.
  • Tzuo-Yau Fan
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan.
  • Chang-Fu Kuo
    Department of Rheumatology, Allergy, and Immunology, Chang Gung Memorial Hospital, Taipei, Taiwan, ROC.
  • Tzu-Yun Yen
    School of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan City, 33302, Taiwan.
  • Szu-Yi Chang
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan.
  • Yu-Cheng Pei
    Department of Physical Medicine and Rehabilitation, Chang Gung Memorial Hospital at Linkou; School of Medicine, Chang Gung University; Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital; Healthy Aging Research Center, Chang Gung University.
  • Yueh-Peng Chen
    Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing St., Guishan Dist., Taoyuan City, 333, Taiwan. yuepengc@gmail.com.

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