Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia.
GIDITIC, Universidad EAFIT, Medellín 050022, Colombia.
Sensors (Basel). 2023 Aug 10;23(16):7072. doi: 10.3390/s23167072.
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.
局灶性皮质发育不良（FCD）是一种与癫痫密切相关的先天性脑畸形。早期、准确的诊断对于有效治疗和管理 FCD 至关重要。磁共振成像（MRI）是评估大脑结构最常用的非侵入性神经影像学方法之一，通常与自动方法结合使用以诊断 FCD。在这篇综述中，我们根据 MRI 将 FCD 识别分为三类：视觉、半自动和全自动方法。通过按照 PRISMA 声明进行系统回顾，我们确定了 65 篇相关论文，这些论文有助于我们了解自动 FCD 识别技术。本综述全面概述了自动 FCD 识别领域的最新技术现状，并强调了在开发使用 MRI 图像进行自动 FCD 诊断的可靠、高效方法方面取得的进展和面临的挑战。该领域的未来发展很可能会导致这些自动识别工具集成到医学图像查看软件中，为神经科医生和放射科医生提供增强的诊断能力。此外，新的 MRI 序列和更高场强扫描仪将为精确的 FCD 特征提供更高的分辨率和解剖细节。本综述总结了自动 FCD 识别的现状，从而有助于加深对 FCD 诊断和管理的理解和推进。
