When Tanish Tyagi published his first research paper a year ago on deep learning to detect dementia, it started a family-driven pursuit. Great-grandparents in his family had suffered from Parkinson's, a genetic disease that affects more than 10 million people worldwide. So the now 16-year-old turned to that next, together with his sister, Riya, 14. The siblings, from Short Hills, New Jersey, published a research paper in the fall about using machine learning to detect Parkinson's disease by focusing on micrographia, a handwriting disorder that's a marker for Parkinson's. They aim to make a model widely accessible so that early detection is possible for people around the world with limited access to clinics.

A type of computational analysis that works to analyze typing patterns may help in detecting motor signs of Parkinson's disease at early stages, the researchers who developed the analysis report. This new method, which appeared to allow researchers to discriminate between people with early Parkinson's and those without the disease, may also speed data collection and analysis of disease states across neurodegenerative ills. The study, "Classification of Short Time Series in Early Parkinson's Disease With Deep Learning of Fuzzy Recurrence Plots," was published in the IEEE/CAA Journal of Automatica Sinica. Objective measures of Parkinson's motor signs are crucial for diagnosing the disease early and correctly, as well as for monitoring progression and assessing treatment response. Early detection of Parkinson's disease (PD) is particularly relevant, as people at early stages of the disease are more likely to benefit from neuroprotective treatments.