{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Schappert R"],"funding":["Deutsche Forschungsgemeinschaft"],"pagination":["1136-1140"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11452802"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(9)"],"pubmed_abstract":["<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."],"journal":["Movement disorders clinical practice"],"pubmed_title":["Automated Video-Based Approach for the Diagnosis of Tourette Syndrome."],"pmcid":["PMC11452802"],"funding_grant_id":["FOR 2698"],"pubmed_authors":["Paulus T","Fudickar S","Becker L","Munchau A","Brugge NS","Li F","Verrel J","Baumer T","Schappert R","Roessner V","Beste C"],"additional_accession":[]},"is_claimable":false,"name":"Automated Video-Based Approach for the Diagnosis of Tourette Syndrome.","description":"<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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2025-04-04T02:30:38.763Z","creation":"2025-04-04T02:30:38.763Z"},"accession":"S-EPMC11452802","cross_references":{"pubmed":["38973244"],"doi":["10.1002/mdc3.14158"]}}