Machine learning is a promising tool for detecting and distinguishing tics from extra movements, according to a study released today at the International Congress of Parkinson's Disease and Movement Disorders® in Copenhagen, Denmark.
COPENHAGEN, Denmark, Aug. 27, 2023 /PRNewswire-PRWeb/ -- Machine learning is a promising tool for detecting and distinguishing tics from extra movements, according to a study released today at the International Congress of Parkinson's Disease and Movement Disorders® in Copenhagen, Denmark.
Distinguishing tics from extra movements can be challenging. With machine learning technology, researchers could potentially reduce time spent analyzing video recordings of people with tic disorders.
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."
View the full-text abstract:
http://www.mdsabstracts.org Reference #:951
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About the 2023 MDS International Congress of Parkinson's Disease and Movement Disorders®:
The MDS International Congress is the premiere annual event to advance the clinical and scientific discipline of Movement Disorders, including Parkinson's disease. Convening thousands of leading clinicians, scientists and other health professionals from around the globe, the International Congress will introduce more than 1,800 scientific abstracts and provide a forum for education and collaboration on latest research findings and state-of-the-art treatment options.
About the International Parkinson and Movement Disorder Society:
The International Parkinson and Movement Disorder Society® (MDS), an international society of more than 11,000 clinicians, scientists, and other healthcare professionals, is dedicated to improving patient care through education and research. For more information about MDS, visit http://www.movementdisorders.org.
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