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Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review.

Authors :
Jiménez-Murillo, David
Castro-Ospina, Andrés Eduardo
Duque-Muñoz, Leonardo
Martínez-Vargas, Juan David
Suárez-Revelo, Jazmín Ximena
Vélez-Arango, Jorge Mario
de la Iglesia-Vayá, Maria
Source :
Sensors (14248220). Aug2023, Vol. 23 Issue 16, p7072. 34p.
Publication Year :
2023

Abstract

Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)—one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain—is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
23
Issue :
16
Database :
Academic Search Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
170908198
Full Text :
https://doi.org/10.3390/s23167072