โก Quick Summary
This systematic review explores the use of deep learning (DL) techniques for localizing the epileptogenic zone (EZ) in patients with drug-resistant epilepsy. The findings indicate that while there are promising results, significant methodological limitations hinder clinical translation.
๐ Key Details
- ๐ Studies Reviewed: 36 studies on DL-assisted EZ localization
- ๐งฉ Focus: Segmenting epileptogenic lesions using structural MRI
- โ๏ธ Predominant Technology: Fully convolutional networks
- ๐ Key Findings: High risk of bias in two-thirds of studies; moderate progress in fine-grained localization tasks
๐ Key Takeaways
- ๐ง Deep learning shows potential in localizing the epileptogenic zone for epilepsy surgery.
- ๐ Methodological concerns limit the applicability of current studies.
- ๐ Focal cortical dysplasia is the most commonly targeted pathology.
- ๐ Multi-center cohorts reported promising EZ detection rates in MRI-negative cases.
- โ ๏ธ Bias and applicability issues were prevalent in many studies.
- ๐ Future research should focus on standardized evaluation frameworks.
- ๐ค Exploration of modern DL practices is essential for advancing this field.
๐ Background
The localization of the epileptogenic zone is crucial for the surgical treatment of drug-resistant epilepsy. Traditional methods often fall short, leading researchers to explore the potential of deep learning and neuroimaging techniques. This systematic review aims to synthesize current evidence and identify gaps in the research that need to be addressed for effective clinical application.
๐๏ธ Study
Conducted by Koutsouvelis et al., this systematic review analyzed studies published up to April 2025 that utilized deep learning for localizing the EZ using neuroimaging data. The authors employed the PROBAST+AI tool to assess bias and applicability, extracting key performance metrics and methodological details from the studies.
๐ Results
Out of the 36 studies reviewed, most concentrated on segmenting epileptogenic lesions through structural MRI. The predominant architecture used was fully convolutional networks. Notably, approximately two-thirds of the studies exhibited a high risk of bias and concerns regarding clinical applicability. However, five studies demonstrated promising EZ detection rates in cases where MRI results were negative, indicating a potential breakthrough in this area.
๐ Impact and Implications
The findings from this review underscore the need for improved methodologies in the application of deep learning for EZ localization. By addressing the identified limitations, future research could significantly enhance surgical outcomes for patients with drug-resistant epilepsy. The integration of advanced DL techniques could pave the way for more accurate and reliable localization methods, ultimately improving patient care and treatment efficacy.
๐ฎ Conclusion
This systematic review highlights the transformative potential of deep learning in localizing the epileptogenic zone for epilepsy surgery. While there are promising results, the methodological limitations must be addressed to facilitate clinical translation. Future research should focus on developing standardized evaluation frameworks and exploring innovative DL practices to enhance the accuracy and reliability of EZ localization.
๐ฌ Your comments
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Localizing the epileptogenic zone using deep learning and neuroimaging: A systematic review.
Abstract
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
Author: [‘Koutsouvelis P’, ‘Ermans SJE’, ‘Volmer L’, ‘Hoeberigs CM’, ‘Brecheisen R’, ‘Eekers DB’, ‘Schijns OEMG’, ‘Dekker A’]
Journal: Seizure
Citation: Koutsouvelis P, et al. Localizing the epileptogenic zone using deep learning and neuroimaging: A systematic review. Localizing the epileptogenic zone using deep learning and neuroimaging: A systematic review. 2026; 137:121-137. doi: 10.1016/j.seizure.2026.03.002