Automated Motor Tic Detection: A Machine Learning Approach

Background Video‐based tic detection and scoring is useful to independently and objectively assess tic frequency and severity in patients with Tourette syndrome. In trained raters, interrater reliability is good. However, video ratings are time‐consuming and cumbersome, particularly in large‐scale studies. Therefore, we developed two machine learning (ML) algorithms for automatic tic detection. Objective The aim of this study was to evaluate the performances of state‐of‐the‐art ML approaches for automatic video‐based tic detection in patients with Tourette syndrome. Methods We used 64 videos of n = 35 patients with Tourette syndrome. The data of six subjects (15 videos with ratings) were used as a validation set for hyperparameter optimization. For the binary classification task to distinguish between tic and no‐tic segments, we established two different supervised learning approaches. First, we manually extracted features based on landmarks, which served as input for a Random Forest classifier (Random Forest). Second, a fully automated deep learning approach was used, where regions of interest in video snippets were input to a convolutional neural network (deep neural network). Results Tic detection F1 scores (and accuracy) were 82.0% (88.4%) in the Random Forest and 79.5% (88.5%) in the deep neural network approach. Conclusions ML algorithms for automatic tic detection based on video recordings are feasible and reliable and could thus become a valuable assessment tool, for example, for objective tic measurements in clinical trials. ML algorithms might also be useful for the differential diagnosis of tics. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. Our article confirms that video‐based motor tic detection using machine learning is possible and feasible. We have developed two separate classifiers, a Random Forest classifier and a deep neural network. We found comparable high F1 scores (up to 82%) in both approaches.