Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.

Miller, Renee; Løkkegaard, Annemette; Ye, Xuehua; Johansen, Mette Niemann; Laczi, Balazs; Horndrup, Mads Peter; Sørensen, Michael Zaucha; Madsen, Rasmus Vestergård; Wolber, Jan; Lonsdale, Markus

doi:10.1002/mdc3.70749

Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.

2026-07-25, Movement disorders clinical practice (10.1002/mdc3.70749)
Renee Miller, Annemette Løkkegaard, Xuehua Ye, Mette Niemann Johansen, Balazs Laczi, Mads Peter Horndrup, Michael Zaucha Sørensen, Rasmus Vestergård Madsen, Jan Wolber, and Markus Lonsdale (?)

Parkinson's disease (PD) progression is highly heterogeneous, complicating clinical management and prognostication. While machine learning models have been developed using research datasets such as Parkinson's Precision Medicine Initiative (PPMI) and Parkinson's Disease Biomarkers Program (PDBP), their clinical translatability is limited due to differences in routinely collected data. The Hoehn and Yahr (H&Y) scale is commonly used in clinical practice to stage PD, yet most predictive models focus on less practical measures.

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Machine Learning Prediction of Hoehn and Yahr Scores at 5-Years Post-I-Ioflupane SPECT Imaging in a Real-World Parkinson's Disease Dataset.