A Novel Video-Based Methodology for Automated Classification of Dystonia and Choreoathetosis in Dyskinetic Cerebral Palsy During a Lower Extremity Task.

Autor:	Haberfehlner H; Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium.; Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium.; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands.; Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands., Roth Z; Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium.; Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium., Vanmechelen I; Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium.; Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium., Buizer AI; Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Rehabilitation Medicine, Amsterdam, The Netherlands.; Amsterdam Movement Sciences, Rehabilitation & Development, Amsterdam, The Netherlands.; Amsterdam UMC, Emma Children's Hospital, Amsterdam, The Netherlands., Jeroen Vermeulen R; Department Neurology, Maastricht University Medical Center, Maastricht, The Netherlands., Koy A; Department of Pediatrics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany., Aerts JM; Department of Computer Science, Mechatronics Research Group (M-Group), KU Leuven Bruges, Distrinet, Bruges, Belgium., Hallez H; Department of Biosystems, Division of Animal and Human Health Engineering, Measure, Model and Manage Bioresponse (M3-BIORES), KU Leuven, Leuven, Belgium., Monbaliu E; Department of Rehabilitation Sciences, KU Leuven Bruges, Bruges, Belgium.; Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium.
Jazyk:	angličtina
Zdroj:	Neurorehabilitation and neural repair [Neurorehabil Neural Repair] 2024 Jul; Vol. 38 (7), pp. 479-492. Date of Electronic Publication: 2024 Jun 06.
DOI:	10.1177/15459683241257522
Abstrakt:	Background: Movement disorders in children and adolescents with dyskinetic cerebral palsy (CP) are commonly assessed from video recordings, however scoring is time-consuming and expert knowledge is required for an appropriate assessment. Objective: To explore a machine learning approach for automated classification of amplitude and duration of distal leg dystonia and choreoathetosis within short video sequences. Methods: Available videos of a heel-toe tapping task were preprocessed to optimize key point extraction using markerless motion analysis. Postprocessed key point data were passed to a time series classification ensemble algorithm to classify dystonia and choreoathetosis duration and amplitude classes (scores 0, 1, 2, 3, and 4), respectively. As ground truth clinical scoring of dystonia and choreoathetosis by the Dyskinesia Impairment Scale was used. Multiclass performance metrics as well as metrics for summarized scores: absence (score 0) and presence (score 1-4) were determined. Results: Thirty-three participants were included: 29 with dyskinetic CP and 4 typically developing, age 14 years:6 months ± 5 years:15 months. The multiclass accuracy results for dystonia were 77% for duration and 68% for amplitude; for choreoathetosis 30% for duration and 38% for amplitude. The metrics for score 0 versus score 1 to 4 revealed an accuracy of 81% for dystonia duration, 77% for dystonia amplitude, 53% for choreoathetosis duration and amplitude. Conclusions: This methodology study yielded encouraging results in distinguishing between presence and absence of dystonia, but not for choreoathetosis. A larger dataset is required for models to accurately represent distinct classes/scores. This study presents a novel methodology of automated assessment of movement disorders solely from video data. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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