Machine Learning Shows Promise for Objective Diagnosis of Parkinson's Disease

Machine learning models were able to differentiate Parkinson's disease, multiple system atrophy, and progressive supranuclear palsy with up to 90% accuracy using PET scans.
A machine learning model using MRI scans and clinical features predicted gait dysfunction in Parkinson's patients with 77.8% accuracy.
The machine learning models show promise for assessing Parkinson's disease and related conditions.
The models need to be validated in larger studies before being implemented in clinical practice.
Machine learning could provide more objective, data-driven methods of diagnosis and assessing Parkinson's symptoms.

medscape.com

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8/21/2023

An AI Scans Eyes to Spot Parkinson’s Years Before Diagnosis

Researchers have developed an AI model that can detect signs of Parkinson's disease in patients' eye scans up to seven years before clinical diagnosis, potentially allowing for early interventions and delaying the progression of the disease.