Machine learning based finite element analysis for personalized prediction of pressure injury risk in patients with spinal cord injury.

Journal: Computer methods and programs in biomedicine
PMID: 39922124

Abstract

BACKGROUND AND OBJECTIVE: Patients with spinal cord injury (SCI), are prone to pressure injury (PI) in the soft tissues of buttocks. Early prediction of PI holds the potential to reduce the occurrence and progression of PI. This study proposes a machine learning model to predict soft tissue stress/strain and evaluate PI risk in SCI patients.

Authors

  • Ke Zhang
    Center for Radiation Oncology, Affiliated Hangzhou Cancer Hospital, Zhejiang University School of Medicine, Hangzhou 310001, China.
  • Yufang Chen
    Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China.
  • Chenglong Feng
    Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital, School of Medicine, Tongji University, Shanghai 201619, China.
  • Xinhao Xiang
    Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 201619, China.
  • Xiaoqing Zhang
    a College of Information Science and Technology , Donghua University , Shanghai , China.
  • Ying Dai
    School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai ,200092, China.
  • Wenxin Niu
    Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai 201619, China.

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