Deep Learning-Assisted Gait Parameter Assessment for Neurodegenerative Diseases: Model Development and Validation.

Journal: Journal of medical Internet research
PMID: 37405831

Abstract

BACKGROUND: Neurodegenerative diseases (NDDs) are prevalent among older adults worldwide. Early diagnosis of NDD is challenging yet crucial. Gait status has been identified as an indicator of early-stage NDD changes and can play a significant role in diagnosis, treatment, and rehabilitation. Historically, gait assessment has relied on intricate but imprecise scales by trained professionals or required patients to wear additional equipment, causing discomfort. Advancements in artificial intelligence may completely transform this and offer a novel approach to gait evaluation.

Authors

  • Yu Jing
    Institute of Software, Chinese Academy of Sciences, Beijing, China.
  • Peinuan Qin
    The University of Melbourne, Melbourne, Australia.
  • Xiangmin Fan
    The Institute of Software, Chinese Academy of Sciences, Beijing, China.
  • Wei Qiang
    Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China.
  • Zhu Wencheng
    Chinese Academy of Sciences - Ruiyi, Beijing, China.
  • Wei Sun
    Sutra Medical Inc, Lake Forest, CA.
  • Feng Tian
    Bioinformatics Graduate Program, and Department of Biomedical Engineering, Boston. University, 24 Cummington Mall, Boston, MA 02215, USA.
  • Dakuo Wang
    IBM Research, Cambridge, MA, United States.

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