What the trained eye cannot see: Detecting movement defects in early stage Parkinson's disease Date: August 15, 2024 Source: University of Florida Summary: Using machine learning to analyze video ...

More information: Rana M. Khalil et al, Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease, Sensors (2024).

Research develops an automated system using machine learning to quantify motor symptoms in Parkinson's disease and predict disease progression, offering new therapeutic insights.

Researchers videotaped facial expressions of 1,452 people, including 391 with Parkinson's disease – 300 who were clinically diagnosed and 91 who self-reported their condition.

AI techniques primarily use machine learning and deep learning algorithms trained on extensive data sets from simple voice recordings from Parkinson’s patients and healthy controls.

A team of researchers at UCLA has developed a high-tech diagnostic pen that can detect signs of Parkinson’s disease with over 96% accuracy.

Researchers have identified a neurochemical signature that sets Parkinson's disease apart from essential tremor—two of the ...

Machine learning technology reveals that high speed movements are the first affected behaviors in early stages of Parkinson’s disease. Levodopa repairs the speed—not the structure ...

Detecting early rising Parkinson's disease (PD) symptoms could improve treatment outcomes by enabling earlier treatment interventions. In a new eNeuro paper, Daniil Berezhnoi, from Georgetown ...