Ronja Schappert, Julius Verrel, Nele Sophie Brügge, Frédéric Li, Theresa Paulus, Leonie Becker, Tobias Bäumer, Christian Beste, Veit Roessner, Sebastian Fudickar, Alexander Münchau
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Automated Video-Based Approach for the Diagnosis of Tourette Syndrome.
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: 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: 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: 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: Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.
期刊介绍:
Movement Disorders Clinical Practice- is an online-only journal committed to publishing high quality peer reviewed articles related to clinical aspects of movement disorders which broadly include phenomenology (interesting case/case series/rarities), investigative (for e.g- genetics, imaging), translational (phenotype-genotype or other) and treatment aspects (clinical guidelines, diagnostic and treatment algorithms)