Researchers turn to AI for help diagnosing Parkinson’s

Accurately diagnosing patients with Parkinson’s-related conditions is key to determining the best possible treatments for patients, as well as developing improved therapies for the future.
Jeff Rowe

As healthcare researchers and providers are finding with emerging variants of COVID-19, it is often difficult to distinguish precisely between a disease’s typical manifestation and related conditions.

For example, three distinct neurodegenerative disorders – Parkinson’s disease, multiple system atrophy Parkinsonian variant (MSAp), and progressive supranuclear palsy (PSP) – can share overlapping motor and non-motor features, like changes in gait, but the three conditions also have critical differences in pathology and prognosis.

With the goal of helping providers distinguish between the three conditions, researchers from the University of Florida (UF) will use a $5 million grant from the National Institutes of Health to test a new AI tool aimed at distinguishing the diagnosis of Parkinson’s from the two related conditions.

Previous research has shown that accuracy in diagnosis of Parkinson’s can be as low as 58 percent, and more than half of misdiagnosed patients actually have one of the two variants.

Testing of the new AI tool will include MRI images from 315 patients following a baseline visit at 21 sites across North America. To differentiate between the forms of Parkinsonism, researchers have developed a novel, noninvasive biomarker technique using diffusion-weighted MRI. This technique measures how water molecules diffuse in the brain and helps identify where neurodegeneration is occurring.

“What is new is the use of artificial intelligence for predicting the type of Parkinsonism,” said Angelos Barmpoutis, Ph.D., an associate professor and coordinator of research and technology at UF’s Digital Worlds Institute and one of the principal investigators.

“In order to train a computer to identify Parkinsonism, we need to teach it using a lot of data. One solution for that is crowd sourcing — going around to different institutes that have patients and asking them to contribute to this big project. We try to collect as many data points by creating what I believe is one of the largest databases for this particular type of diagnosis.”

In addition, a UF neurologist and University of Chicago neurologist who specialize in movement disorders will assess video of each patient and their clinical background to evaluate the diagnosis. Each participant will then be reevaluated clinically 18 months later to confirm their diagnosis.

“This isn’t going to replace the physician’s decision making; it’s just meant to be another tool in their toolkit,” said David Vaillancourt, Ph.D., a professor and chair of the UF College of Health & Human Performance’s department of applied physiology and kinesiology. “The goal is that clinical trials will be better because they will focus on specific variants. Patients will be able to know their diagnosis earlier.”

The team’s ultimate goal is to gain approval from the FDA for use of the tool as a clinically approved diagnostic marker to adequately distinguish between forms of Parkinsonism.

Patient recruitment is scheduled to begin at 19 US sites and two in Canada by summer and continue for two years.