Automatic differentiation of Parkinson’s disease motor subtypes based on deep learning and radiomics
The BDT model based on MRI radiomics features constructed in this study exhibited excellent performance in differentiating motor subtypes of Parkinson's disease and can serve as an effective tool for clinical auxiliary diagnosis. This fully automated model is capable of processing MRI data and providing results within 3 min, offering an efficient and reliable solution for the early differentiation of Parkinson's disease motor subtypes, with significant clinical application value.
Authors
- Yi Guo (ORCID: https://orcid.org/0000-0003-2727-9809)
- Lu Xie (ORCID: https://orcid.org/0000-0001-7541-2243)
- Dongming Hui
- Fengxi Chen (ORCID: https://orcid.org/0000-0002-1097-2036)
- Yaxi Luo (ORCID: https://orcid.org/0000-0001-7198-8720)
- Xia Wang (ORCID: https://orcid.org/0000-0002-5809-5191)
- Xiaojing He
Institutions
- Army Medical University (CN)
- Dalian Medical University (CN)
- Second Affiliated Hospital of Chongqing Medical University (CN)
- Southwest Hospital (CN)
- Chongqing Emergency Medical Center (CN)
- Community Health Center (US)
- First People's Hospital of Chongqing (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-09-04
- DOI
- https://doi.org/10.3389/fneur.2025.1650985
- Citations
- 1
- Primary Topic
- Parkinson's Disease Mechanisms and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.39