Machine Learning Algorithm Shows Ability to Distinguish Tic From NonTic Movements

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Despite the challenge of distinguishing tics from extra movements, machine learning technology could potentially help researchers with reducing time spent analyzing video recordings of patients with tic disorders.

A recent study presented at the 2023 International Congress of Parkinson’s Disease and Movement Disorders in Copenhagen, Denmark, highlights the potential of machine learning in distinguishing tics from other movements in patients with tic disorders. The study achieved an impressive 83% accuracy rate using the Random Forest classifier on a dataset of 63 patient videos. Dr. Davide Martino of the University of Calgary praised the study’s potential in routine clinical practice and research. Further refinements are in progress to enhance the tic detection algorithm’s accuracy and extend its applicability beyond facial/head tics.

Source NeurologyLive

Neurologica
Author: Neurologica