Cervical myelopathy screening focusing on finger motion using noncontact sensor
Application of machine learning for early diagnosis and treatment of a
disease

Date:
October 13, 2021
Source:
Japan Science and Technology Agency
Summary:
Researchers have developed a simple screening tool using
a non-contact sensor for Cervical myelopathy (CM) combining a
finger motion analysis technique and machine learning. The tool
allows for non-specialists to screen people for the possibility
of having CM. The screening test results can be used to encourage
those with suspected CM to seek specialist's attention for early
diagnosis and early treatment initiation.



FULL STORY ========================================================================== Cervical myelopathy (CM)(1) results from compression of the spinal
cord in the neck and causes difficulty moving the fingers and unsteady
gait. As patients with early-stage CM have minimal subjective symptoms
and are difficult for non- specialists to diagnose properly, the
symptoms can be aggravated before patients are diagnosed with CM by a specialist. Therefore, the development of screening tools is required
to realize the early diagnosis and treatment of CM.


==========================================================================
A research team led by Drs. Koji Fujita, a lecturer at Tokyo Medical
and Dental University, and Yuta Sugiura, an associate professor at
Keio University, combined a finger motion analysis technique using a non-contact sensor and machine learning (2) to develop a simple screening
tool for CM.

In this study, the team focused on changes in finger motion caused by
CM. In the 10-second grip and release test, which is a conventional
diagnostic test for CM, a subject repeats grip and release actions as
many times as possible in 10 seconds. The test simply measures the
number of grip and release actions and does not focus on changes in
finger movements characteristic for patients with CM, such as wrist
movements to compensate for difficulty moving the finger.

Leap Motion (Ultraleap Ltd.), a sensor capable of real-time measurement
of finger movements, can be used to extract such movements more
precisely. The researchers expected that CM can be predicted using machine learning combined with the Leap Motion sensor. A subject sitting in
front of Leap Motion connected to a laptop computer with arms extended
was instructed to grip and release the fingers 20 times as rapidly as
possible. Finger movements during this test were captured by the Leap
Motion sensor, displayed on its screen in real time, and recorded as
data. They recruited 50 patients with CM and 28 subjects who did not
have CM. Time-series data on their finger movements acquired by Leap
Motion were converted into frequency domains, which were subjected to
machine learning using a support vector machine. Finally, the accuracy
of the results was high as indicated by a sensitivity (3) of 84.0%, a specificity (4) of 60.7%, and an area under the curve (5) of 0.85. This
level of accuracy is equivalent or superior to that of CM diagnosis by specialists based on physical findings.

The tool developed by the team allows for non-specialists to screen
people for the possibility of having CM. The screening test results
can be used to encourage those with suspected CM to seek specialist's
attention for early diagnosis and early treatment initiation. A goal of
this research is to prevent disease aggravation which can cause decline
in the physical functioning and social loss.

This research has been conducted under the JST Strategic Basic Research programs, AIP Accelerated PRISM research and Precursory Research for
Embryonic Science and Technology (PRSTO).

(1) Cervical myelopathy (CM): CM is a disease resulted from compression
of the spinal cord in the neck. CM patients are formally diagnosed with cervical spondylotic myelopathy or cervical ossification of the posterior longitudinal ligament depending on the cause.

(2) Machine learning: A mechanism through which computers learn about
a given task and automatically calculate the results.

(3) Sensitivity: The proportion of subjects with a disease who test
positive for the disease.

(4) Specificity: The proportion of subjects without a disease who test
negative for the disease.

(5) Area Under Curve (AUC): A measure for evaluation of test methods,
ranging from 0 to 1. An AUC closer to 1 means that the test method has
high accuracy.

========================================================================== Story Source: Materials provided by
Japan_Science_and_Technology_Agency. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Takafumi Koyama, Koji Fujita, Masaru Watanabe, Kaho Kato, Toru
Sasaki,
Toshitaka Yoshii, Akimoto Nimura, Yuta Sugiura, Hideo Saito,
Atsushi Okawa. Cervical Myelopathy Screening with Machine Learning
Algorithm Focusing on Finger Motion Using Non-Contact Sensor. Spine,
2021 DOI: 10.1097/BRS.0000000000004243 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/10/211013104629.htm

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