Harnessing routine MRI for the early screening of Parkinson’s disease: a multicenter machine learning study using T2-weighted FLAIR imaging
Conventional head MRI is routinely performed in Parkinson's disease (PD) but exhibits inadequate specificity for diagnosis. Machine learning (ML) models based on conventional T2W FLAIR images showed favorable accuracy for PD diagnosis. ML algorithm enables early screening of PD on routine T2W FLAIR sequence.
Authors
- Kun Lv (ORCID: https://orcid.org/0000-0003-4967-3727)
- Daoying Geng (ORCID: https://orcid.org/0000-0002-3585-6883)
- Chengling Xu (ORCID: https://orcid.org/0000-0001-8854-9281)
- Haiyan Zhang
- Qingqing Lu (ORCID: https://orcid.org/0000-0001-5602-0148)
- Junyan Fu
- Zhongzheng Jia
- Hongyi Chen (ORCID: https://orcid.org/0000-0003-2458-6854)
- Jun Zhang (ORCID: https://orcid.org/0000-0001-6990-5324)
- Yue Hu (ORCID: https://orcid.org/0009-0005-3553-8454)
Institutions
- Ningbo University (CN)
- Shanghai Medical College of Fudan University (CN)
- Xuzhou Medical College (CN)
- Nantong University (CN)
- Fudan University (CN)
- Affiliated Hospital of Nantong University (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Huashan Hospital (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-04-26
- DOI
- https://doi.org/10.1186/s13244-025-01961-3
- Citations
- 4
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 5.56
Funders
- National Natural Science Foundation of China
- Science and Technology Commission of Shanghai Municipality