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Deep learning based markerless motion tracking as a clinical tool for movement disorders: Utility, feasibility and early experience

Authors :
Rex N. Tien
Anand Tekriwal
Dylan J. Calame
Jonathan P. Platt
Sunderland Baker
Lauren C. Seeberger
Drew S. Kern
Abigail L. Person
Steven G. Ojemann
John A. Thompson
Daniel R. Kramer
Source :
Frontiers in Signal Processing, Vol 2 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

Clinical assessments of movement disorders currently rely on the administration of rating scales, which, while clinimetrically validated and reliable, depend on clinicians’ subjective analyses, resulting in interrater differences. Intraoperative microelectrode recording for deep brain stimulation targeting similarly relies on clinicians’ subjective evaluations of movement-related neural activity. Digital motion tracking can improve the diagnosis, assessment, and treatment of movement disorders by generating objective, standardized measures of patients’ kinematics. Motion tracking with concurrent neural recording also enables motor neuroscience studies to elucidate the neurophysiology underlying movements. Despite these promises, motion tracking has seen limited adoption in clinical settings due to the drawbacks of conventional motion tracking systems and practical limitations associated with clinical settings. However, recent advances in deep learning based computer vision algorithms have made accurate, robust markerless motion tracking viable in any setting where digital video can be captured. Here, we review and discuss the potential clinical applications and technical limitations of deep learning based markerless motion tracking methods with a focus on DeepLabCut (DLC), an open-source software package that has been extensively applied in animal neuroscience research. We first provide a general overview of DLC, discuss its present usage, and describe the advantages that DLC confers over other motion tracking methods for clinical use. We then present our preliminary results from three ongoing studies that demonstrate the use of DLC for 1) movement disorder patient assessment and diagnosis, 2) intraoperative motor mapping for deep brain stimulation targeting and 3) intraoperative neural and kinematic recording for basic human motor neuroscience.

Details

Language :
English
ISSN :
26738198
Volume :
2
Database :
Directory of Open Access Journals
Journal :
Frontiers in Signal Processing
Publication Type :
Academic Journal
Accession number :
edsdoj.91b46a6b8abc43d6b3769e4ad2cb657e
Document Type :
article
Full Text :
https://doi.org/10.3389/frsip.2022.884384