New Machine Learning Method Identify Subtle Movement Deficits in Early Parkinson's Disease, Study Reveals
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A new research published in the journal Parkinsonism and Related Disorders, suggests that the use of machine learning (ML) can detect movement deficits in early-stage Parkinson’s disease from the videos. This technique measures motor symptoms in the early stages of Parkinson's disease and could uncover signs of the condition and other movement disorders sooner, potentially leading to improved treatment outcomes.
A research team from the University of Florida and the Fixel Institute for Neurological Diseases has demonstrated that using video analysis can aid in identifying early signs of Parkinsonism in individuals. Their method involves comparing the movement of the left and right sides of a person's body. According to the researchers, this technique leverages the characteristic asymmetry of Parkinson’s disease, which typically begins with more pronounced symptoms on one side of the body compared to the other in the initial stages. This asymmetrical onset allows the video-based approach to detect subtle differences in movement that may indicate the early presence of the disease.
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