Automated Video‐Based Approach for the Diagnosis of Tourette Syndrome

Abstract

AbstractBackgroundThe occurrence of tics is the main basis for the diagnosis of Gilles de la Tourette syndrome (GTS). Video‐based tic assessments are time consuming.ObjectiveThe 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.MethodsThe 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.ResultsVideos 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%.ConclusionsAutomated video‐based methods have a great potential to support quantitative assessment and clinical decision‐making in tic disorders.