Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor.
Study Design: Cross-sectional study.Objective: To develop a binary classification model for cervical myelopathy (CM) screening based on a machine learning algorithm using Leap Motion (Leap Motion, San Francisco, CA), a novel noncontact sensor device.Summary Of Background Data: Progress of CM symptom...
| Published in: | Spine (03622436) Vol. 47; no. 2; pp. 163 - 172 |
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| Main Authors: | , , , , , , , , , |
| Format: | research Journal Article |
| Published: |
Lippincott Williams & Wilkins
1/15/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=154054111&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154054111 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03622436 1Y6 jtl: Spine (03622436) issn: 03622436 maglogo: N pubinfo: dt: 1/15/2022 vid: 47 iid: 2 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 154054111 154054111 NLM34593737 154054111 10.1097/BRS.0000000000004243 NLM34593737 154054111 ppf: 163 ppct: 9 formats: tig: atl: Cervical Myelopathy Screening with Machine Learning Algorithm Focusing on Finger Motion Using Noncontact Sensor. aug: au: Koyama, Takafumi Fujita, Koji Watanabe, Masaru Kato, Kaho Sasaki, Toru Yoshii, Toshitaka Nimura, Akimoto Sugiura, Yuta Saito, Hideo Okawa, Atsushi affil: Department of Orthopaedic and Spinal Surgery, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Japan sug: subj: Spinal Cord Diseases Diagnosis Cervical Vertebrae Cross Sectional Studies Upper Extremity Treatment Outcomes Human ab: Study Design: Cross-sectional study.Objective: To develop a binary classification model for cervical myelopathy (CM) screening based on a machine learning algorithm using Leap Motion (Leap Motion, San Francisco, CA), a novel noncontact sensor device.Summary Of Background Data: Progress of CM symptoms are gradual and cannot be easily identified by the patients themselves. Therefore, screening methods should be developed for patients of CM before deterioration of myelopathy. Although some studies have been conducted to objectively evaluate hand movements specific to myelopathy using cameras or wearable sensors, their methods are unsuitable for simple screening outside hospitals because of the difficulty in obtaining and installing their equipment and the long examination time.Methods: In total, 50 and 28 participants in the CM and control groups were recruited, respectively. The diagnosis of CM was made by spine surgeons. We developed a desktop system using Leap Motion that recorded 35 parameters of fingertip movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used to develop the binary classification model, and a multiple linear regression analysis was performed to create regression models to estimate the total Japanese Orthopaedic Association (JOA) score and the JOA score of the motor function of the upper extremity (MU-JOA score).Results: The binary classification model indexes were as follows: sensitivity, 84.0%; specificity, 60.7%; accuracy, 75.6%; area under the curve, 0.85. The Spearman rank correlation coefficient between the estimated score and the total JOA score was 0.44 and that between the estimated score and the MU-JOA score was 0.51.Conclusion: Our binary classification model using a machine learning algorithm and Leap Motion could classify CM with high sensitivity and would be useful for CM screening in daily life before consulting doctors and telemedicine.Level of Evidence: 3. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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