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

ABSTRACT:

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. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an ind

SUBMITTER: Gill RS 

PROVIDER: S-EPMC8548962 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature