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).

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.

Wearable sensor data combined with machine learning predicts fall risk in Parkinson's patients, enhancing preventive care and clinical outcomes over five years.

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 ...

Tongue coating analysis reveals potential biomarkers for early Parkinson's disease detection, offering a non-invasive and cost-effective diagnostic approach.

An international team, led by researchers at the Champalimaud Foundation (CF), has shown—for the first time in a realistic way—that it may be possible to diagnose Parkinson's disease (PD ...

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 ...