Localizing the epileptogenic zone using deep learning and neuroimaging: A systematic review

Deep learning (DL) techniques may support localizing the epileptogenic zone (EZ) and improve surgical outcomes in drug-resistant epilepsy. This systematic review synthesizes current evidence on DL-assisted EZ localization from neuroimaging acquisitions, aiming to outline methodological trends, limitations, and future directions that bridge the gap between clinical translation and technological advances.

We systematically searched PubMed, Scopus, and Embase (via Ovid) on April 15, 2025, for studies applying DL to localize the EZ using neuroimaging data. The bias and applicability of studies was assessed using the PROBAST+AI tool. We extracted methodological details, as well as key performance metrics.

Thirty-six studies met the eligibility criteria, most focusing on segmenting epileptogenic lesions using structural MRI. Focal cortical dysplasia was the most commonly targeted pathology, with fully convolutional networks being the predominant DL architecture. Approximately two-thirds of the studies showed high risk of bias and clinical applicability concerns, limited by non-representative cohorts and suboptimal evaluation methods. Five studies reported promising EZ detection rate in MRI-negative cases using large multi-center cohorts, yet progress in fine-grained localization tasks, such as lesion segmentation, remained moderate.

This review highlights methodological limitations hindering the clinical translation of current DL approaches for EZ localization and provides a comprehensive set of recommendations to address them. Future work should prioritize developing standardized, clinically informative evaluation frameworks and explore research avenues aligned with modern DL practices, spanning from uncertainty quantification to large-scale vision foundation models and synthetic data generation.