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10.3390/s23167072
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Review

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

by
David Jiménez-Murillo
1,
Andrés Eduardo Castro-Ospina
1,*,
Leonardo Duque-Muñoz
1,
Juan David Martínez-Vargas
2,
Jazmín Ximena Suárez-Revelo
3,
Jorge Mario Vélez-Arango
3 and
Maria de la Iglesia-Vayá
4,5
1
Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia
2
GIDITIC, Universidad EAFIT, Medellín 050022, Colombia
3
Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia
4
Biomedical Imaging Unit FISABIO-CIPF, Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Avda. de Catalunya, 21, 46020 Valencia, Spain
5
Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM-G23), 28029 Madrid, Spain
*
Author to whom correspondence should be addressed.
Sensors 2023, 23(16), 7072; https://doi.org/10.3390/s23167072
Submission received: 8 July 2023 / Revised: 2 August 2023 / Accepted: 7 August 2023 / Published: 10 August 2023
(This article belongs to the Special Issue Biomedical Data and Imaging: Sensing, Understanding and Applications)
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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.
Keywords: deep learning; focal cortical dysplasia; image processing; machine learning; magnetic resonance imaging deep learning; focal cortical dysplasia; image processing; machine learning; magnetic resonance imaging

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MDPI and ACS Style

Jiménez-Murillo, D.; Castro-Ospina, A.E.; Duque-Muñoz, L.; Martínez-Vargas, J.D.; Suárez-Revelo, J.X.; Vélez-Arango, J.M.; de la Iglesia-Vayá, M. Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review. Sensors 2023, 23, 7072. https://doi.org/10.3390/s23167072

AMA Style

Jiménez-Murillo D, Castro-Ospina AE, Duque-Muñoz L, Martínez-Vargas JD, Suárez-Revelo JX, Vélez-Arango JM, de la Iglesia-Vayá M. Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review. Sensors. 2023; 23(16):7072. https://doi.org/10.3390/s23167072

Chicago/Turabian Style

Jiménez-Murillo, David, Andrés Eduardo Castro-Ospina, Leonardo Duque-Muñoz, Juan David Martínez-Vargas, Jazmín Ximena Suárez-Revelo, Jorge Mario Vélez-Arango, and Maria de la Iglesia-Vayá. 2023. "Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review" Sensors 23, no. 16: 7072. https://doi.org/10.3390/s23167072

APA Style

Jiménez-Murillo, D., Castro-Ospina, A. E., Duque-Muñoz, L., Martínez-Vargas, J. D., Suárez-Revelo, J. X., Vélez-Arango, J. M., & de la Iglesia-Vayá, M. (2023). Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review. Sensors, 23(16), 7072. https://doi.org/10.3390/s23167072

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