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Over 10 million people around the world are living with Parkinsons disease, according to the Parkinsons Foundation. The causes of this neurodegenerative disorder also remain largely unknown.

But, artificial intelligence is helping expand scientists understanding of this complex condition and how to treat it.

Using machine learning techniques, researchers at Weill Cornell Medicine have identified three new distinct subtypes of Parkinsons disease based on the rate of symptom progression. The discovery could lead to more tailored treatments based on individual patient symptoms.

Parkinsons disease is highly heterogenous, which means that people with the same disease can have very different symptoms, explained Dr. Fei Wang, the studys senior author.

This indicates there is not likely to be a one-size-fits-all approach to treating it. We may need to consider customized treatment strategies based on a patients disease subtype.

The three new subtypes are known as Inching Pace, Moderate Pace, and Rapid Pace.

The Inching Pace (PD-I) subtype affects approximately 36% of patients and has mild symptoms that progress gradually. The Moderate Pace (PD-M) subtype affects approximately 51% of patients and begins with milder symptoms that progress at a moderate pace. Lastly, the Rapid Pace (PD-R) subtype progresses the quickest.

The researchers used deep learning a form of artificial intelligence capable of analyzing massive datasets to uncover patterns that might elude human detection to discover these subtypes.

By examining anonymous clinical records from two different sizable databases, the researchers identified these three distinct patterns of Parkinsons progression.

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Researchers Used Artificial Intelligence To Identify Three New Distinct Subtypes Of Parkinsons Disease In A Groundbreaking New Study - Chip Chick