Severity Classification of Pediatric Spinal Cord Injuries Using Structural MRI Measures and Deep Learning: A Comprehensive Analysis across All Vertebral Levels
The study demonstrates the effectiveness of integrating cross-sectional structural imaging measures with deep learning methods for classification and severity assessment of pediatric SCI. The deep learning approach outperforms traditional machine learning models in diagnostic accuracy, offering potential improvements in patient care in pediatric SCI management.
Authors
- Adam E. Flanders (ORCID: https://orcid.org/0000-0002-4679-0787)
- Laura Krisa (ORCID: https://orcid.org/0000-0002-7345-4922)
- Mahdi Alizadeh (ORCID: https://orcid.org/0000-0003-1923-0407)
- Devon Middleton (ORCID: https://orcid.org/0000-0002-6437-2159)
- Feroze B. Mohamed (ORCID: https://orcid.org/0000-0002-0764-0612)
- Sara Naghizadehkashani
- Ze Wang (ORCID: https://orcid.org/0000-0002-8339-5567)
- Scott H. Faro (ORCID: https://orcid.org/0000-0003-1521-8718)
- Zahra Sadeghi-Adl
Institutions
- University of Maryland, Baltimore (US)
- Thomas Jefferson University (US)
- Temple University (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.3174/ajnr.a8770
- Citations
- 3
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.02