Publication date: Mar 25, 2025
Novel ways to improve diagnosis are needed. In the current study, researchers investigated whether MRI imaging alongside machine learning could help differentiate between different varieties of Parkinsons disease. AI-driven software was able to differentiate between typical and atypical forms of Parkinson’s disease with 96% accuracy. Ultimately, AIDP differentiated between typical Parkinson’s disease and atypical parkinsonism with 96% accuracy. We look forward to seeing how this innovation can further impact the Parkinson’s community and advance our shared goal of better outcomes for all. ” said study author Michael Okun, M. D., director of the Norman Fixel Institute for Neurological Diseases at the University of Florida, in a press release. Sources: Science Daily, JAMA Neurology The corresponding study was published in JAMA Neurology. “This effort truly highlights the importance of interdisciplinary collaboration.
| Concepts | Keywords |
|---|---|
| Daily | Accuracy |
| Florida | Atypical |
| Mri | Conditions |
| Parkinsonism | Diagnosis |
| Scientific | Differentiate |
| Expertise | |
| Florida | |
| Jama | |
| Neurology | |
| Parkinson | |
| Parkinsons | |
| Press | |
| Release | |
| Typical |
Semantics
| Type | Source | Name |
|---|---|---|
| disease | MESH | Parkinson’s disease |
| pathway | REACTOME | Release |
| disease | MESH | progressive supranuclear palsy |
| disease | MESH | Parkinsonism |
| drug | DRUGBANK | Water |
| drug | DRUGBANK | Tropicamide |