Literature DB >> 28475202

Quantitative assessment of hand motor function in cervical spinal disorder patients using target tracking tests.

Sunghoon I Lee1, Alex Huang2, Bobak Mortazavi2, Charles Li1, Haydn A Hoffman2, Jordan Garst2, Derek S Lu2, Ruth Getachew2, Marie Espinal2, Mehrdad Razaghy2, Nima Ghalehsari2, Brian H Paak2, Amir A Ghavam2, Marwa Afridi2, Arsha Ostowari2, Hassan Ghasemzadeh1, Daniel C Lu2,3, Majid Sarrafzadeh1.   

Abstract

Cervical spondylotic myelopathy (CSM) is a chronic spinal disorder in the neck region. Its prevalence is growing rapidly in developed nations, creating a need for an objective assessment tool. This article introduces a system for quantifying hand motor function using a handgrip device and target tracking test. In those with CSM, hand motor impairment often interferes with essential daily activities. The analytic method applied machine learning techniques to investigate the efficacy of the system in (1) detecting the presence of impairments in hand motor function, (2) estimating the perceived motor deficits of CSM patients using the Oswestry Disability Index (ODI), and (3) detecting changes in physical condition after surgery, all of which were performed while ensuring test-retest reliability. The results based on a pilot data set collected from 30 patients with CSM and 30 nondisabled control subjects produced a c-statistic of 0.89 for the detection of impairments, Pearson r of 0.76 with p < 0.001 for the estimation of ODI, and a c-statistic of 0.82 for responsiveness. These results validate the use of the presented system as a means to provide objective and accurate assessment of the level of impairment and surgical outcomes.

Entities:  

Keywords:  cervical spondylotic myelopathy; classifier; hand impairment; hand movement; machine learning; motor deficit; patient monitoring; quantification; spinal cord disorder; tracking test

Mesh:

Year:  2016        PMID: 28475202     DOI: 10.1682/JRRD.2014.12.0319

Source DB:  PubMed          Journal:  J Rehabil Res Dev        ISSN: 0748-7711


  5 in total

Review 1.  Utility of machine learning algorithms in degenerative cervical and lumbar spine disease: a systematic review.

Authors:  Mark E Stephens; Christen M O'Neal; Alison M Westrup; Fauziyya Y Muhammad; Daniel M McKenzie; Andrew H Fagg; Zachary A Smith
Journal:  Neurosurg Rev       Date:  2021-09-07       Impact factor: 3.042

Review 2.  If it's information, it's not "bias": a scoping review and proposed nomenclature for future response-shift research.

Authors:  Carolyn E Schwartz; Gudrun Rohde; Elijah Biletch; Richard B B Stuart; I-Chan Huang; Joseph Lipscomb; Roland B Stark; Richard L Skolasky
Journal:  Qual Life Res       Date:  2021-10-27       Impact factor: 4.147

3.  Changes in reaching skill in patients with cervical spondylosis after cervical decompression surgery.

Authors:  Naoto Noguchi; Bumsuk Lee; Ken Kondo; Masatake Ino; Shoya Kamiya; Tsuneo Yamazaki
Journal:  J Phys Ther Sci       Date:  2019-10-19

Review 4.  Artificial Intelligence Technologies in Neurosurgery: a Systematic Literature Review Using Topic Modeling. Part II: Research Objectives and Perspectives.

Authors:  G V Danilov; M A Shifrin; K V Kotik; T A Ishankulov; Yu N Orlov; A S Kulikov; A A Potapov
Journal:  Sovrem Tekhnologii Med       Date:  2020-12-28

Review 5.  Machine Learning in Orthopedics: A Literature Review.

Authors:  Federico Cabitza; Angela Locoro; Giuseppe Banfi
Journal:  Front Bioeng Biotechnol       Date:  2018-06-27
  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.