Epilepsy Redefined: Cutting-Edge Machine Learning and Software Tools for Diagnosis and Treatment

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting millions of individuals worldwide. Traditional diagnostic and treatment approaches often rely on subjective assessments and limited data sources, leading to challenges in accurate seizure localization, choice of treatment, and prognosis. However, recent advances in machine learning (ML), artificial intelligence (AI), and other software tools provide opportunities to address these limitations and revolutionize the management of epilepsy.

The goal of this Research Topic is to explore the potential of ML and AI techniques and other software tools in improving the understanding, diagnosis, treatment, and prognosis of epilepsy. We aim to address the current challenges by utilizing large and accurate multimodal data to extract meaningful patterns and insights from various sources, including clinical semiology, brain imaging (e.g., MRI), and electroencephalogram (EEG) signals.

We hope to enhance the integrated management of epilepsy by providing evidence-based individualized treatment recommendations, assistance in the presurgical workup for drug-resistant epilepsy, automated seizure localization and lateralization, surgical prognostication, and even developing models for seizure risk forecasting.

We invite researchers to contribute original manuscripts that focus on the intersection of epilepsy and machine learning and digital software. Potential themes to address within this Research Topic include, but are not limited to:

-AI and ML models that surpass current clinical standards in epilepsy management
-Clinical semiology and its relevance in ML-based seizure characterization
-Algorithmic localization and lateralization of epileptic foci
-Multimodal integration approaches, combining data from MRI, EEG, and clinical semiology, e.g. as part of a presurgical workup to localize the Epileptogenic Zone
-Prognostication models for epilepsy surgery outcomes
-Seizure risk forecasting using machine learning techniques

We welcome research articles, reviews, opinions, and case studies that demonstrate innovation in the field of epilepsy research and clinical practice with the aim of ultimately enhancing the quality of life for individuals with epilepsy.

Keywords: Epilepsy, Eplielpsy management, Machine learning, Seizure localization, Seizure risk forecasting

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.