The study by Becker and colleagues used a dataset of 63 videos of people with tic disorders to train a Random Forest classifier to identify tic movements. Various predictable features were combined into a single detection tic score and trained using videos from both people with tic disorders and healthy controls. The accuracy of this tic score in classifying patients and healthy controls was 83%.
“The frequency and characteristic cluster aggregation of tics are key determinants of tic severity,” said Dr. Davide Martino, Professor of Neurology at the University of Calgary. “Wearable sensors recording tics in patients’ natural environment are currently under exploration, but the anatomical distribution and diverse phenomenology of tics hinder the routine clinical applicability of these sensors. Tic frequency and phenomenology are also routinely assessed using video recordings usually obtained in a clinical setting, a methodology often used also in clinical trials. Rating these recordings is time- and energy-consuming. This study applies machine learning to train an algorithm that classifies tics from non-tic extra movements and measures several parameters detailing the temporal distribution of tics, ultimately combining these into a single tic detection score. The study reports a very good classification accuracy of the algorithm (83%), although the composition and accuracy of the tic detection score is still in progress.
An algorithm that measures frequency and clustering of tics from video recordings has strong translational value in routine clinical practice and clinical research, as it would likely optimize reliability and efficiency of these measurements. Although limited to facial/head tics, the same approach can be extended to other body regions and phonic tics. It is also important to point out that video recording-based measures will inevitably still need to be integrated with other domains of tic severity, e.g., interference with daily routines and functional impact, in order to achieve a truly comprehensive assessment of tics.”
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http://www.mdsabstracts.org Reference #:951
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