Automated Video-Based Approach for the Diagnosis of Tourette Syndrome.
Tourette
automated
tic detection
video based
Journal
Movement disorders clinical practice
ISSN: 2330-1619
Titre abrégé: Mov Disord Clin Pract
Pays: United States
ID NLM: 101630279
Informations de publication
Date de publication:
07 Jul 2024
07 Jul 2024
Historique:
received:
06
06
2024
accepted:
20
06
2024
medline:
8
7
2024
pubmed:
8
7
2024
entrez:
8
7
2024
Statut:
aheadofprint
Résumé
The occurrence of tics is the main basis for the diagnosis of Gilles de la Tourette syndrome (GTS). Video-based tic assessments are time consuming. The aim was to assess the potential of automated video-based tic detection for discriminating between videos of adults with GTS and healthy control (HC) participants. The quantity and temporal structure of automatically detected tics/extra movements in videos from adults with GTS (107 videos from 42 participants) and matched HCs were used to classify videos using cross-validated logistic regression. Videos were classified with high accuracy both from the quantity of tics (balanced accuracy of 87.9%) and the number of tic clusters (90.2%). Logistic regression prediction probability provides a graded measure of diagnostic confidence. Expert review of about 25% of lower-confidence predictions could ensure an overall classification accuracy above 95%. Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.
Sections du résumé
BACKGROUND
BACKGROUND
The occurrence of tics is the main basis for the diagnosis of Gilles de la Tourette syndrome (GTS). Video-based tic assessments are time consuming.
OBJECTIVE
OBJECTIVE
The aim was to assess the potential of automated video-based tic detection for discriminating between videos of adults with GTS and healthy control (HC) participants.
METHODS
METHODS
The quantity and temporal structure of automatically detected tics/extra movements in videos from adults with GTS (107 videos from 42 participants) and matched HCs were used to classify videos using cross-validated logistic regression.
RESULTS
RESULTS
Videos were classified with high accuracy both from the quantity of tics (balanced accuracy of 87.9%) and the number of tic clusters (90.2%). Logistic regression prediction probability provides a graded measure of diagnostic confidence. Expert review of about 25% of lower-confidence predictions could ensure an overall classification accuracy above 95%.
CONCLUSIONS
CONCLUSIONS
Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Deutsche Forschungsgemeinschaft
ID : FOR 2698
Informations de copyright
© 2024 The Author(s). Movement Disorders Clinical Practice published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Références
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