Automated differentiation for parkinsonism using clinical structural MRI: A multicenter study

Multimodal research-grade magnetic resonance imaging (MRI) scans show potential for differentiating Parkinson’s disease (PD) from multiple system atrophy (MSA), but clinical translation is limited by cost, acquisition time, and processing burden. Here, we developed a T1-weighted structural MRI-based machine learning model to differentiate Parkinsonism and generalized it to routine anisotropic 2D clinical MRI. Using a multicenter cohort of 969 participants, we employed a hierarchical discovery–verification–translation framework. While a multimodal benchmark model achieved an area under the receiver operating characteristic curve (AUROC) of 0.98, our streamlined unimodal T1-Top20 model achieved an AUROC of 0.93 for multiclass discrimination (healthy controls, PD, MSA). For PD vs MSA, the frozen T1-Top20 model achieved a pooled AUROC of 0.88 in the external research-grade cohort ( n = 227) and identified 19 of 25 who phenoconverted from idiopathic rapid eye movement sleep behavior disorder (iRBD) to PD. Importantly, the generalized model achieved a pooled AUROC of 0.84 in the clinical-MRI cohort (2D T1, n = 303), supporting its potential for clinical translation.

Authors

Institutions

Publication Details

Journal
npj Digital Medicine
Published
2026-10-16
DOI
https://doi.org/10.1038/s41746-026-03357-z
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automated differentiation for parkinsonism using clinical structural MRI: A multicenter study

周立彻, Yong Lü, 毛成洁, Jin‐Ru Zhang et al.
npj Digital Medicine
Parkinson's Disease Mechanisms and Treatments
article

Automated differentiation for parkinsonism using clinical structural MRI: A multicenter study

周立彻, Yong Lü, 毛成洁, Jin‐Ru Zhang, Ailian Du, Kezhong Zhang, Jun Liu, Chun‐Feng Liu, Haiying Lyu, Chuanying Xu, Jie Wang, Zihao Lin, Jinfeng Wu, Tao Wu, Chengzhe Wang, Xiaoyu Cheng, Sijia Huang, Guiyun Cui, Xue Zhu, Dongling Zhang
article en

Abstract

Multimodal research-grade magnetic resonance imaging (MRI) scans show potential for differentiating Parkinson’s disease (PD) from multiple system atrophy (MSA), but clinical translation is limited by cost, acquisition time, and processing burden. Here, we developed a T1-weighted structural MRI-based machine learning model to differentiate Parkinsonism and generalized it to routine anisotropic 2D clinical MRI. Using a multicenter cohort of 969 participants, we employed a hierarchical discovery–verification–translation framework. While a multimodal benchmark model achieved an area under the receiver operating characteristic curve (AUROC) of 0.98, our streamlined unimodal T1-Top20 model achieved an AUROC of 0.93 for multiclass discrimination (healthy controls, PD, MSA). For PD vs MSA, the frozen T1-Top20 model achieved a pooled AUROC of 0.88 in the external research-grade cohort ( n = 227) and identified 19 of 25 who phenoconverted from idiopathic rapid eye movement sleep behavior disorder (iRBD) to PD. Importantly, the generalized model achieved a pooled AUROC of 0.84 in the clinical-MRI cohort (2D T1, n = 303), supporting its potential for clinical translation.

npj Digital Medicine
Shanghai Jiao Tong University (CN), Capital Medical University (CN), Ruijin Hospital (CN), Soochow University (CN), Beijing Tian Tan Hospital (CN), Affiliated Hospital of Xuzhou Medical College (CN), Tongren Hospital (CN), Second Affiliated Hospital of Soochow University (CN), Songjiang District Central Hospital (CN), Jiangsu Province Hospital (CN), Nanjing Medical University (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China, Shanghai Jiao Tong University, Allergan, Avid Radiopharmaceuticals
Good health and well-being
Openalex Percentile: Top 14%
Parkinson's Disease Mechanisms and Treatments
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.