Automated identification of clinically meaningful biomechanical phenotypes in cerebral palsy through multicenter gait data.
Cerebral palsy is the most prevalent motor disability in childhood, encompassing various movement disorders that affect walking. Researchers have described gait patterns in cerebral palsy, but these are often subjective and based on clinician experience. This study introduces an automated approach t...
| Publicado en: | Clinical Biomechanics Vol. 125 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
May2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184996201&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184996201 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02680033 JB1 jtl: Clinical Biomechanics issn: 02680033 maglogo: N pubinfo: dt: May2025 vid: 125 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 184996201 184996201 184996201 10.1016/j.clinbiomech.2025.106501 184996201 ppct: 1 formats: tig: atl: Automated identification of clinically meaningful biomechanical phenotypes in cerebral palsy through multicenter gait data. aug: au: Graf, Adam Krzak, Joseph J. Kruger, Karen M. Davids, Jon Smith, Ryan Steinlein, Brandon Bagley, Anita affil: Motion Analysis Center Engineer, Shriners Children's Chicago, 2211 North Oak Park Avenue, Chicago, IL 60707, United States of America sug: subj: Automation Biomechanics Evaluation Phenotype Classification Cerebral Palsy Physiopathology Gait Analysis Gait Disorders, Neurologic Therapy Quality Improvement Classification Algorithms Human Multicenter Studies Gait Disorders, Neurologic Physiopathology Software Persons with Disabilities Medical Practice, Evidence-Based Individualized Medicine Decision Making, Clinical ab: Cerebral palsy is the most prevalent motor disability in childhood, encompassing various movement disorders that affect walking. Researchers have described gait patterns in cerebral palsy, but these are often subjective and based on clinician experience. This study introduces an automated approach to objectively identify clinically meaningful biomechanical phenotypes in cerebral palsy and test it on multicenter gait data. Utilizing instrumented gait analysis, this research aims to improve treatment strategies for gait dysfunction. This study addresses whether classification algorithms can objectively identify clinically meaningful gait patterns and if severe gait deviations are more frequent in advanced forms of cerebral palsy. Two novel classification algorithms (sagittal and transverse planes) were developed and automated in Python. These were based on previous work and refined using clinical expertise and data from four motion analysis centers in the Shriners Children's system, including 700 patients with cerebral palsy. The patient's gait data was applied to the treatment algorithms, and the percentage of each phenotype is presented. Findings. Novel sagittal and transverse plane gait phenotype algorithms were created. When applied to the cerebral palsy cohort, we found that more severe gait deviations, or combinations of deviations, were more apparent in the more severe forms of cerebral palsy. Interpretations. Classifying a patient's biomechanical phenotype provides valuable insights into therapeutic interventions. The results allow for the automation of data-driven classification algorithms, leading to efficient, accurate, and reliable classifications of biomechanical phenotypes that support evidence-based, personalized treatment decisions and clinical management. • Novel algorithms for sagittal and transverse plane gait phenotypes developed. • Multicenter study includes gait data from 700 cerebral palsy patients. • Severe gait deviations are more frequent in advanced cerebral palsy cases. • Automated classification of cerebral palsy gait patterns may improve treatment strategies. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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