Application of Neuroimaging in Diagnosis of Focal Cortical Dysplasia: A Survey of Computational Techniques

Focal Cortical Dysplasia (FCD) is a neurodevelopmental disorder characterized by abnormal neuronal migration, differentiation, and maturation, resulting in a range of clinical symptoms including drug-resistant epilepsy. Accurate and timely diagnosis of FCD is essential for effective treatment and management of patients with this condition. In recent years, there have been significant advances in neuroimaging and machine learning techniques, enabling automated detection of FCD lesions with increasing accuracy and efficiency.In this survey article, we provide an overview of the current state-of-the-art in computational techniques for the automated detection of FCD lesions using neuroimaging. We first introduce some common imaging findings that radiologists look for in FCD lesions, then review the automatic detection techniques studied in the last decade. These techniques have shown promising results in detecting and localizing FCD lesions, improving diagnostic accuracy and reducing misdiagnoses, diagnosis time, and cost of care.We suggest that the use of these types of quantitative image analysis tools and algorithms can play a key role in improving the overall management and outcome of patients with FCD. However, further studies are needed to validate and optimize the performance of these techniques in clinical practice. In summary, the combination of advanced neuroimaging and computational techniques has the potential to revolutionize the diagnosis and treatment of FCD, leading to better outcomes for patients with this condition.