The use of automated and AI ‐driven algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia

• Published in: Epilepsia (Copenhagen) Vol. 66; no. S3; pp. 64 - 71
• Main Authors: Bernasconi, Andrea, Gill, Ravnoor S., Bernasconi, Neda
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.09.2025
• Subjects: Algorithms; Artificial Intelligence; Drug Resistant Epilepsy - diagnostic imaging; Dysplasia; Epilepsy; Focal Cortical Dysplasia; Hippocampal Sclerosis; Hippocampus; Hippocampus - diagnostic imaging; Hippocampus - pathology; Humans; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Malformations of Cortical Development - diagnostic imaging; Malformations of Cortical Development - pathology; Neuroimaging; Sclerosis; Sclerosis - diagnostic imaging; Seizures; Spatial discrimination; epilepsy; focal cortical dysplasia; temporal lobe epilepsy; artificial intelligence
• ISSN: 0013-9580; 1528-1167; 1528-1157; 1528-1167
• DOI: 10.1111/epi.17989

In drug‐resistant epilepsy, magnetic resonance imaging (MRI) plays a central role in detecting lesions as it offers unmatched spatial resolution and whole‐brain coverage. In addition, the last decade has witnessed continued developments in MRI‐based computer‐aided machine‐learning techniques for improved diagnosis and prognosis. In this review, we focus on automated algorithms for the detection of hippocampal sclerosis and focal cortical dysplasia, particularly in cases deemed as MRI negative, with an emphasis on studies with histologically validated data. In addition, we discuss imaging‐derived prognostic markers, including response to anti‐seizure medication, post‐surgical seizure outcome, and cognitive reserves. We also highlight the advantages and limitations of these approaches and discuss future directions toward person‐centered care.