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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/16/7072 |
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| author | David Jiménez-Murillo Andrés Eduardo Castro-Ospina Leonardo Duque-Muñoz Juan David Martínez-Vargas Jazmín Ximena Suárez-Revelo Jorge Mario Vélez-Arango Maria de la Iglesia-Vayá |
| author_facet | David Jiménez-Murillo Andrés Eduardo Castro-Ospina Leonardo Duque-Muñoz Juan David Martínez-Vargas Jazmín Ximena Suárez-Revelo Jorge Mario Vélez-Arango Maria de la Iglesia-Vayá |
| author_sort | David Jiménez-Murillo |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T23:36:53Z |
| format | Article |
| id | doaj.art-ac87e4c02dbb453cb4b540216bb548fc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:36:53Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ac87e4c02dbb453cb4b540216bb548fc2023-11-19T02:56:23ZengMDPI AGSensors1424-82202023-08-012316707210.3390/s23167072Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic ReviewDavid Jiménez-Murillo0Andrés Eduardo Castro-Ospina1Leonardo Duque-Muñoz2Juan David Martínez-Vargas3Jazmín Ximena Suárez-Revelo4Jorge Mario Vélez-Arango5Maria de la Iglesia-Vayá6Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, ColombiaGrupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, ColombiaGrupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, ColombiaGIDITIC, Universidad EAFIT, Medellín 050022, ColombiaGrupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, ColombiaGrupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, ColombiaBiomedical Imaging Unit FISABIO-CIPF, Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Avda. de Catalunya, 21, 46020 Valencia, SpainFocal 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.https://www.mdpi.com/1424-8220/23/16/7072deep learningfocal cortical dysplasiaimage processingmachine learningmagnetic resonance imaging |
| spellingShingle | David Jiménez-Murillo Andrés Eduardo Castro-Ospina Leonardo Duque-Muñoz Juan David Martínez-Vargas Jazmín Ximena Suárez-Revelo Jorge Mario Vélez-Arango Maria de la Iglesia-Vayá Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review Sensors deep learning focal cortical dysplasia image processing machine learning magnetic resonance imaging |
| 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 | deep learning focal cortical dysplasia image processing machine learning magnetic resonance imaging |
| url | https://www.mdpi.com/1424-8220/23/16/7072 |
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