AI-driven software is 96% accurate at diagnosing Parkinson's

Existing research indicates that the accuracy of a Parkinson’s disease diagnosis hovers between 55% and 78% in the first five years of assessment. That’s partly because Parkinson’s sibling movement disorders share similarities, sometimes making a definitive diagnosis initially difficult.

Although Parkinson’s disease is a well-recognized illness, the term can refer to a variety of conditions, ranging from idiopathic Parkinson’s, the most common type, to other movement disorders like multiple system atrophy Parkinsonian variant and progressive supranuclear palsy. Each shares motor and nonmotor features, like changes in gait — but possess a distinct pathology and prognosis.

Roughly one in four patients, or even one in two patients, is misdiagnosed.

Now, researchers at the University of Florida and the UF Health Norman Fixel Institute for Neurological Diseases have developed a new kind of software that will help clinicians differentially diagnose Parkinson’s disease and related conditions, reducing diagnostic time and increasing precision beyond 96%. The study was published recently in JAMA Neurology and was funded by the National Institutes of Health.