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A team of researchers from the University of Oxford has shown for the first time that it is possible to track the progression of Parkinson’s Disease accurately using specially trained machine learning algorithms to analyse data derived from sensor devices worn by patients.

A black women doctor at the consultation with and old man © Shutterstock

The novel methods described in this study led by Professor Chrystalina Antoniades in Oxford's Nuffield Department of Clinical Neurosciences can be used by clinicians alongside the more traditional clinical rating scales to not only improve the accuracy of diagnosis, but also track progression of Parkinson's Disease.

Being able to monitor the progression of motor symptoms in people with neurological disorders such as Parkinson's disease is important for two main reasons: clinicians need to be confident in their assessment of how the disease is progressing in individual patients, and researchers running clinical trials need to be able to measure how well therapeutic interventions are working.


Read the full story on the University of Oxford's website.