Artificial intelligence provides an opportunity to try to redefine disease subtypes based on
similar pathobiology. Using a machine-learning algorithm (Subtype and Stage Inference) …

Objective In temporal lobe epilepsy (TLE), although hippocampal atrophy lateralizes the
focus, the value of magnetic resonance imaging (MRI) to predict postsurgical outcome is …

Artificial intelligence has recently gained popularity across different medical fields to aid in
the detection of diseases based on pathology samples or medical imaging findings. Brain …

Artificial intelligence (AI)-based tools are widely employed, but their use for diagnosis and
prognosis of neurological disorders is still evolving. Here we analyse a cross-sectional …

Mesial temporal lobe epilepsy (mTLE), the most common type of focal epilepsy, is
associated with functional and structural brain alterations. Machine learning (ML) techniques …

Volumetric and morphometric studies have demonstrated structural abnormalities related to
chronic epilepsies on a cohort-and population-based level. On a single-patient level …

Background Radiological identification of temporal lobe epilepsy (TLE) is crucial for
diagnosis and treatment planning. TLE neuroimaging abnormalities are pervasive at the …

Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial
temporal sclerosis. Identifying these MRI features is critical for the diagnosis and …

In drug-resistant temporal lobe epilepsy, precise predictions of drug response, surgical
outcome and cognitive dysfunction at an individual level remain challenging. A possible …

Background and purpose Novel approaches applying machine-learning methods to
neuroimaging data seek to develop individualized measures that will aid in the diagnosis …