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Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor.

Takafumi Koyama1, Koji Fujita2, Masaru Watanabe3

  • 1Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Japan.

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A new machine learning model using Leap Motion technology can effectively screen for cervical myelopathy (CM). This non-contact device offers a sensitive tool for early CM detection in daily life, aiding telemedicine and pre-consultation assessments.

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Area of Science:

  • Biomedical Engineering
  • Machine Learning in Healthcare
  • Neurology

Background:

  • Cervical myelopathy (CM) symptoms progress gradually, often unnoticed by patients.
  • Current objective hand movement evaluation methods are complex and unsuitable for simple, out-of-hospital screening.
  • There is a need for accessible screening tools to detect CM before significant deterioration.

Purpose of the Study:

  • To develop a binary classification model for cervical myelopathy (CM) screening.
  • To utilize a machine learning algorithm with the novel Leap Motion noncontact sensor device.
  • To create a user-friendly and accessible screening method for CM.

Main Methods:

  • A cross-sectional study involving 50 CM patients and 28 controls.
  • Development of a desktop system using Leap Motion to record fingertip movement parameters.
  • A support vector machine was employed to build the binary classification model.

Main Results:

  • The classification model achieved 84.0% sensitivity, 60.7% specificity, and 75.6% accuracy.
  • The area under the curve (AUC) for the model was 0.85.
  • Correlation coefficients between estimated and actual scores (JOA and MU-JOA) ranged from 0.44 to 0.51.

Conclusions:

  • The developed binary classification model effectively identifies CM using machine learning and Leap Motion.
  • This model demonstrates high sensitivity and utility for daily life CM screening.
  • The system is valuable for early detection before medical consultation and for telemedicine applications.