Artificial intelligence has the potential to revolutionize modern society in all its aspects.
Encouraged by the variety and vast amount of data that can be gathered from patients (eg …

Abstract Machine learning leverages statistical and computer science principles to develop
algorithms capable of improving performance through interpretation of data rather than …

One outstanding challenge for machine learning in diagnostic biomedical imaging is
algorithm interpretability. A key application is the identification of subtle epileptogenic focal …

Abstract Objective Ultra‐high‐field 7‐Tesla (7T) magnetic resonance imaging (MRI) offers
increased signal‐to‐noise and contrast‐to‐noise ratios, which may improve visualization of …

Objective Drug‐resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs).
The distribution of these lesions across the cerebral cortex and the impact of lesion location …

The rapid progress in artificial intelligence, machine learning, and natural language
processing has led to increasingly sophisticated large language models (LLMs) for use in …

Drug‐resistant epilepsy (DRE) considerably affects patient health, cognition, and well‐
being, and disproportionally contributes to the overall burden of epilepsy. The most common …

Abstract Machine learning is playing an increasingly important role in medical image
analysis, spawning new advances in the clinical application of neuroimaging. There have …

Abstract Objective Focal cortical dysplasias (FCDs) are a common cause of drug‐resistant
focal epilepsy but frequently remain undetected by conventional magnetic resonance …

Human neuroimaging has had a major impact on the biological understanding of epilepsy
and the relationship between pathophysiology, seizure management, and outcomes. This …