{Reference Type}: Journal Article
{Title}: Automated Video-Based Approach for the Diagnosis of Tourette Syndrome.
{Author}: Schappert R;Verrel J;Brügge NS;Li F;Paulus T;Becker L;Bäumer T;Beste C;Roessner V;Fudickar S;Münchau A;
{Journal}: Mov Disord Clin Pract
{Volume}: 0
{Issue}: 0
{Year}: 2024 Jul 7
{Factor}: 4.514
{DOI}: 10.1002/mdc3.14158
{Abstract}: <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.