Machine learning models for classifying non-specific neck pain using craniocervical posture and movement.

Journal: Musculoskeletal science & practice
Published Date: Jun 1, 2024

Abstract

OBJECTIVE: Physical therapists and clinicians commonly confirm craniocervical posture (CCP), cervical retraction, and craniocervical flexion as screening tests because they contribute to non-specific neck pain (NSNP). We compared the predictive performance of statistical machine learning (ML) models for classifying individuals with and without NSNP using datasets containing CCP and cervical kinematics during pro- and retraction (CKdPR).

Authors

  • Ui-Jae Hwang
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Oh-Yun Kwon
    Laboratory of Kinetic Ergocise Based on Movement Analysis, Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Jun-Hee Kim
    Laboratory of KEMA AI Research (KAIR), Department of Physical Therapy, College of Software and Digital Healthcare Convergence, Yonsei University, Wonju, South Korea.
  • Sejung Yang
    Medical Physics Division, Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, United States of America.

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