Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia.

Background And Objective: To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).

Methods: We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus.

Automated Online Quantification Method for F-FDG Positron Emission Tomography/CT Improves Detection of the Epileptogenic Zone in Patients with Pharmacoresistant Epilepsy.

Aims: To assess the validity of an online method to quantitatively evaluate cerebral hypometabolism in patients with pharmacoresistant focal epilepsy as a complement to the visual analysis of the F-FDG positron emission tomography (PET)/CT exam.

Methods: A total of 39 patients with pharmacoresistant epilepsy and probable focal cortical dysplasia [22 patients with frontal lobe epilepsy (FLE) and 17 with temporal lobe epilepsy (TLE)] underwent a presurgical evaluation including EEG, video-EEG, MRI, and F-FDG PET/CT. We conducted the automated quantification of their F-FDG PET/CT data and compared the results with those of the visual-PET analysis conducted by experienced nuclear medicine physicians.