Proposing a Three-Stage Model to Quantify Bradykinesia on a Symptom Severity Level Using Deep Learning

Jaber, R.; Qahwaji, Rami; Abdullatif, Amr; Buckley, John; Abd‐Alhameed, Raed A.

doi:10.1007/978-3-030-87094-2_38

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Such studies have been performed with inertial sensors (Burkhard et al, 1999;Hoff et al, 2001;Salarian et al, 2007;Giuffrida et al, 2009;Patel et al, 2009;Kim et al, 2011;Pulliam et al, 2014;Ramsperger et al, 2016;Delrobaei et al, 2017;Jeon et al, 2017), electromagnetic sensors (Espay et al, 2009;Gao et al, 2018), infrared markers (Das et al, 2011) and smartwatches (Malekmohammadi et al, 2016;Lo´pez-Blanco et al, 2019). Similar studies have also been performed using markerless methods (Li M. H. et al, 2018;Liu et al, 2019;Sato et al, 2019;Wong et al, 2019;Williams et al, 2020a;Pang et al, 2020;Jaber et al, 2021;Shin et al, 2021). We identified four studies that used DLC for movement disorder diagnosis and/or assessment.…”

Section: Study 1: Background and Related Workmentioning

confidence: 71%

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

Tien

Tekriwal²,

Calame³

et al. 2022

Front. Signal Process.

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Clinical assessments of movement disorders currently rely on the administration of rating scales, which, while clinimetrically validated and reliable, rely 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.

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Section: Study 1: Background and Related Workmentioning

confidence: 71%

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

Tien

Tekriwal²,

Calame³

et al. 2022

Front. Signal Process.

View full text Add to dashboard Cite

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“…With a similar purpose in mind, Jaber et al [29] sought to show how CV can be a suitable framework for PD assessment. The research showed a way of capturing FT movements using CV libraries (i.e., LabelImg and YOLO [30]) and transforming them into valuable metrics and features that can help with PD diagnostics.…”

Section: Related Workmentioning

confidence: 99%

Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping

Amo-Salas,

Olivares-Gil,

García-Bustillo

et al. 2024

Healthcare

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Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up.

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Proposing a Three-Stage Model to Quantify Bradykinesia on a Symptom Severity Level Using Deep Learning

Cited by 2 publications

References 11 publications

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

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

Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping

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