Impact of Nigrosome-1 MRI and Deep Learning Models in Parkinsonism: Clinical Follow-up At Year-3

Motivation: Diagnosing parkinsonism is challenging, especially in early stages, with misdiagnosis rates around 25%. Nigrosome-1 MRI and deep learning (DL) models may improve diagnostic accuracy. Goal(s): To report the clinical utility of nigrosome-1 MRI and DL models for automated nigrosome-1 classification and quantification against long-term clinical outcomes in patients presenting with parkinsonism. Approach: Retrospective review of patient records for clinical outcomes at year-3. Results: Radiological assessment, Heuron IPD binary classification and Heuron NI nigrosome-1 quantification showed strong concordance with clinical outcomes (AUC: radiologist=0.87, Heuron NI=0.84, Heuron IPD=0.82). Volume analysis with Heuron NI distinguished idiopathic Parkinson's disease from non-neurodegenerative conditions (p<0.005). Impact: Nigrosome-1 MRI in the radiologic clinic aids in differentiating idiopathic Parkinson's disease from non-neurodegenerative conditions when clinical presentation is unclear. Deep learning models enhance accessibility and show good potential as objective adjunctive imaging tools easing radiological workflow for accurate diagnosis.