Application of machine learning algorithms to identify people with low bone density.

Journal: Frontiers in public health
PMID: 38726233

Abstract

BACKGROUND: Osteoporosis is becoming more common worldwide, imposing a substantial burden on individuals and society. The onset of osteoporosis is subtle, early detection is challenging, and population-wide screening is infeasible. Thus, there is a need to develop a method to identify those at high risk for osteoporosis.

Authors

  • Rongxuan Xu
    Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
  • Yongxing Chen
    Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
  • Zhihan Yao
    Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
  • Wei Wu
    Department of Pharmacy, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
  • Jiaxue Cui
    Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
  • Ruiqi Wang
    Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
  • Yizhuo Diao
    Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
  • Chenxin Jin
    Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.
  • Zhijun Hong
    The Health Management Center, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning, China.
  • Xiaofeng Li
    Department of Otorhinolaryngology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai 200233, China.

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