Result: Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders.

Title:
Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders.
Authors:
Hu H; University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, Vancouver, BC, Canada.; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada., Xiao D; University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, Vancouver, BC, Canada.; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada., Rhodin H; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada., Murphy TH; University of British Columbia, Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Detwiller Pavilion, Vancouver, BC, Canada.; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada.
Source:
Journal of Parkinson's disease [J Parkinsons Dis] 2022 Aug 27; Vol. 1 (-1), pp. 2085-2096.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: SAGE Publications Country of Publication: Netherlands NLM ID: 101567362 Publication Model: Print Cited Medium: Internet ISSN: 1877-718X (Electronic) Linking ISSN: 18777171 NLM ISO Abbreviation: J Parkinsons Dis Subsets: MEDLINE
Imprint Name(s):
Publication: 2024- : [Thousand Oaks, CA] : SAGE Publications
Original Publication: Amsterdam : IOS Press
MeSH Terms:
Parkinson Disease*, Biomarkers ; Humans ; Machine Learning ; Movement ; Neural Networks, Computer
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Grant Information:
FDN-143209 Canada CIHR; PJT-180631 Canada CIHR
Contributed Indexing:
Keywords: Artificial intelligence; Parkinson’s disease; computer-assisted diagnosis; computer-assisted image processing; movement disorders; neural networks (computer)
Substance Nomenclature:
0 (Biomarkers)
Entry Date(s):
Date Created: 20220904 Date Completed: 20220908 Latest Revision: 20230904
Update Code:
20250114
PubMed Central ID:
PMC10473142
DOI:
10.3233/JPD-223351
PMID:
36057831
Database:
MEDLINE

Further Information

Human motion analysis has been a common thread across modern and early medicine. While medicine evolves, analysis of movement disorders is mostly based on clinical presentation and trained observers making subjective assessments using clinical rating scales. Currently, the field of computer vision has seen exponential growth and successful medical applications. While this has been the case, neurology, for the most part, has not embraced digital movement analysis. There are many reasons for this including: the limited size of labeled datasets, accuracy and nontransparent nature of neural networks, and potential legal and ethical concerns. We hypothesize that a number of opportunities are made available by advancements in computer vision that will enable digitization of human form, movements, and will represent them synthetically in 3D. Representing human movements within synthetic body models will potentially pave the way towards objective standardized digital movement disorder diagnosis and building sharable open-source datasets from such processed videos. We provide a perspective of this emerging field and describe how clinicians and computer scientists can navigate this new space. Such digital movement capturing methods will be important for both machine learning-based diagnosis and computer vision-aided clinical assessment. It would also supplement face-to-face clinical visits and be used for longitudinal monitoring and remote diagnosis.