Scientists from UNSW Sydney with collaborators at Boston University have developed a tool that shows early promise in detecting Parkinson’s disease years before the first symptoms start appearing.

Parkinson’s disease (PD) is growing more rapidly than any other neurological disease, which makes its early detection so important. Researchers have developed a new machine-learning tool that ...

The use of machine learning models identified three distinct subtypes of Parkinson’s disease, which could have immediate implications in detecting clinical outcomes.

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

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).

Their work, published today in Nature Machine Intelligence, has shown that computer models can accurately classify four subtypes of Parkinson's disease, with one reaching an accuracy of 95%.

Although examination and observation by a trained physician is currently the gold standard of Parkinson’s symptom evaluation, automated methods using wearable sensors and machine learning models are ...

Researchers have developed a new method to detect early signs of Parkinson's disease by monitoring movements of patients using available wearable technology like Apple Watches and Fitbits.