Carotid plaque segmentation and classification using MRI-based plaque texture analysis and convolutional neural network
This study establishes a fully automated, hybrid framework that synergizes segmentation and classification to advance stroke risk stratification. By reducing manual dependency and inter-observer variability, our approach enhances reproducibility and generalizability across diverse clinical datasets. The statistically significant ROC-AUC and high accuracy underscore its potential as an AI-driven diagnostic tool, paving the way for standardized, data-driven cerebrovascular disease management.
Authors
- Maryam Ghaffar
- Gehad Abdullah Amran (ORCID: https://orcid.org/0000-0002-4304-4199)
- Ali A. AL-Bakhrani (ORCID: https://orcid.org/0000-0003-1360-8640)
- Abdalla Ibrahim (ORCID: https://orcid.org/0000-0003-4138-5755)
- Amerah Alabrah
- Saleh Abdul Amir Mohammad
- Lubna Alkhalil
- Zakarya Hasan Ahmed Abu Alregal
Institutions
- Central South University (CN)
- Dalian University of Technology (CN)
- King Saud University (SA)
- Khwaja Fareed University of Engineering and Information Technology (PK)
- Changsha Central Hospital (CN)
- University of South China (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-06-20
- DOI
- https://doi.org/10.3389/fmed.2025.1502830
- Citations
- 1
- Primary Topic
- Cerebrovascular and Carotid Artery Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- King Saud University
- Deanship of Scientific Research, King Saud University