Evaluation of vascular cognitive impairment and identification of imaging markers using machine learning: a multimodal MRI study
Our study developed and validated accurate ML models for VCI detection, emphasizing the importance of DTI. The identified imaging markers, particularly those derived from DTI, underscoring the potential in enhancing diagnostic accuracy and understanding cognitive impairments related to vascular changes.
Authors
- Xiaoxiang Peng (ORCID: https://orcid.org/0000-0001-6339-0462)
- Ping Zhang (ORCID: https://orcid.org/0000-0002-3907-1127)
- Jiu Jiang
- Haoying He (ORCID: https://orcid.org/0009-0003-4575-0227)
- Fan Fan (ORCID: https://orcid.org/0000-0001-5853-2761)
- Dong Sun (ORCID: https://orcid.org/0000-0003-3945-4037)
- T. Sun (ORCID: https://orcid.org/0000-0003-2994-4526)
- Ming Lei (ORCID: https://orcid.org/0000-0001-9818-3586)
- Junjian Zhang (ORCID: https://orcid.org/0000-0002-4557-5137)
- Zhipeng Xu (ORCID: https://orcid.org/0000-0002-6661-5572)
- Sisi Peng
- Dongwei Lu
Institutions
- Wuhan University (CN)
- Zhongnan Hospital of Wuhan University (CN)
- Hubei Zhongshan Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-05-29
- DOI
- https://doi.org/10.3389/fneur.2025.1505739
- Citations
- 2
- Primary Topic
- Cerebrovascular and Carotid Artery Diseases
- Type
- article
- Field-Weighted Citation Impact
- 3.04