biostudies-literatureSchappert RDeutsche Forschungsgemeinschaft1136-1140https://www.ebi.ac.uk/biostudies/studies/S-EPMC11452802biostudies-literatureUnknown11(9)<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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%.<h4>Conclusions</h4>Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.Movement disorders clinical practiceAutomated Video-Based Approach for the Diagnosis of Tourette Syndrome.PMC11452802FOR 2698Paulus TFudickar SBecker LMunchau ABrugge NSLi FVerrel JBaumer TSchappert RRoessner VBeste CfalseAutomated Video-Based Approach for the Diagnosis of Tourette Syndrome.<h4>Background</h4>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.<h4>Objective</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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%.<h4>Conclusions</h4>Automated video-based methods have a great potential to support quantitative assessment and clinical decision-making in tic disorders.2024-01-01T00:00:00Z2024 Sep2025-04-04T02:30:38.763Z2025-04-04T02:30:38.763ZS-EPMC114528023897324410.1002/mdc3.14158