%0 Journal Article
%T Automated Video-Based Approach for the Diagnosis of Tourette Syndrome.
%A Schappert R
%A Verrel J
%A Brügge NS
%A Li F
%A Paulus T
%A Becker L
%A Bäumer T
%A Beste C
%A Roessner V
%A Fudickar S
%A Münchau A
%J Mov Disord Clin Pract
%V 0
%N 0
%D 2024 Jul 7
%M 38973244
%F 4.514
%R 10.1002/mdc3.14158
%X <B>BACKGROUND: </B>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.<BR><B>OBJECTIVE: </B>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.<BR><B>METHODS: </B>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.<BR><B>RESULTS: </B>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%.<BR><B>CONCLUSIONS: </B>Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.