Validation and application of computer vision algorithms for video-based tremor analysis.

Autor: Friedrich MU; Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Boston, MA, USA. mfriedrich1@bwh.harvard.edu.; Harvard Medical School, Boston, MA, USA. mfriedrich1@bwh.harvard.edu.; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany. mfriedrich1@bwh.harvard.edu., Roenn AJ; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany., Palmisano C; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany., Alty J; Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, Tasmania, Australia., Paschen S; Department of Neurology, University Kiel, Kiel, Germany., Deuschl G; Department of Neurology, University Kiel, Kiel, Germany., Ip CW; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany., Volkmann J; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany., Muthuraman M; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany., Peach R; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany.; Department of Brain Sciences, Imperial College, London, UK., Reich MM; Department of Neurology, University Hospital Wurzburg, Wuerzburg, Germany. reich_m1@ukw.de.
Jazyk: angličtina
Zdroj: NPJ digital medicine [NPJ Digit Med] 2024 Jun 21; Vol. 7 (1), pp. 165. Date of Electronic Publication: 2024 Jun 21.
DOI: 10.1038/s41746-024-01153-1
Abstrakt: Tremor is one of the most common neurological symptoms. Its clinical and neurobiological complexity necessitates novel approaches for granular phenotyping. Instrumented neurophysiological analyses have proven useful, but are highly resource-intensive and lack broad accessibility. In contrast, bedside scores are simple to administer, but lack the granularity to capture subtle but relevant tremor features. We utilise the open-source computer vision pose tracking algorithm Mediapipe to track hands in clinical video recordings and use the resulting time series to compute canonical tremor features. This approach is compared to marker-based 3D motion capture, wrist-worn accelerometry, clinical scoring and a second, specifically trained tremor-specific algorithm in two independent clinical cohorts. These cohorts consisted of 66 patients diagnosed with essential tremor, assessed in different task conditions and states of deep brain stimulation therapy. We find that Mediapipe-derived tremor metrics exhibit high convergent clinical validity to scores (Spearman's ρ = 0.55-0.86, p≤ .01) as well as an accuracy of up to 2.60 mm (95% CI [-3.13, 8.23]) and ≤0.21 Hz (95% CI [-0.05, 0.46]) for tremor amplitude and frequency measurements, matching gold-standard equipment. Mediapipe, but not the disease-specific algorithm, was capable of analysing videos involving complex configurational changes of the hands. Moreover, it enabled the extraction of tremor features with diagnostic and prognostic relevance, a dimension which conventional tremor scores were unable to provide. Collectively, this demonstrates that current computer vision algorithms can be transformed into an accurate and highly accessible tool for video-based tremor analysis, yielding comparable results to gold standard tremor recordings.
(© 2024. The Author(s).)
Databáze: MEDLINE
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