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
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.

Identifiants

pubmed: 38973244
doi: 10.1002/mdc3.14158
doi:

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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Auteurs

Ronja Schappert (R)

Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany.

Julius Verrel (J)

Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany.

Nele Sophie Brügge (NS)

Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.
German Research Center for Artificial Intelligence, Lübeck, Germany.

Frédéric Li (F)

Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.

Theresa Paulus (T)

Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany.
Department of Neurology, University Medical Center Schleswig-Holstein, Lübeck, Germany.

Leonie Becker (L)

Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany.
Department of Pediatrics, University Medical Center Schleswig-Holstein, Lübeck, Germany.

Tobias Bäumer (T)

Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany.
Lübeck Centre for Rare Diseases, University Medical Center Schleswig-Holstein, Lübeck, Germany.

Christian Beste (C)

Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany.
Faculty of Medicine, University Neuropsychology Center, TU Dresden, Dresden, Germany.
Cognitive Psychology, Faculty of Psychology, Shandong Normal University, Jinan, China.

Veit Roessner (V)

Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany.

Sebastian Fudickar (S)

Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.

Alexander Münchau (A)

Institute of Systems Motor Science, University of Lübeck, Lübeck, Germany.
Lübeck Centre for Rare Diseases, University Medical Center Schleswig-Holstein, Lübeck, Germany.

Classifications MeSH