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Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review
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 evalua...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458261/ https://www.ncbi.nlm.nih.gov/pubmed/37631608 http://dx.doi.org/10.3390/s23167072 |
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author | 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 |
author_facet | 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 |
author_sort | Jiménez-Murillo, David |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10458261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104582612023-08-27 Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review 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 Sensors (Basel) Review 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. MDPI 2023-08-10 /pmc/articles/PMC10458261/ /pubmed/37631608 http://dx.doi.org/10.3390/s23167072 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review 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 Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review |
title | Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review |
title_full | Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review |
title_fullStr | Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review |
title_full_unstemmed | Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review |
title_short | Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review |
title_sort | automatic detection of focal cortical dysplasia using mri: a systematic review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458261/ https://www.ncbi.nlm.nih.gov/pubmed/37631608 http://dx.doi.org/10.3390/s23167072 |
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