About this item:

48 Views | 42 Downloads

Author Notes:

Dr. Ezequiel Gleichgerrcht, MD, PhD Department of Neurology, Medical University of South Carolina 96 Jonathan Lucas St. CSB 301 MSC 606, Charleston, SC 29425, USA E-mail: gleichge@musc.du

The authors report no competing interests.


Research Funding:

This study was supported by grants from the National Institute of Neurological Disorders and Stroke (NINDS) 1R01NS110347-01A (LB, DLD, RK) and R21 NS107739 (LB, BM, CM).


  • artificial intelligence
  • convoluted neural network
  • structural neuroimaging
  • temporal lobe epilepsy

Radiological identification of temporal lobe epilepsy using artificial intelligence: a feasibility study.

Show all authors Show less authors


Journal Title:

Brain Commun


Volume 4, Number 2


, Pages fcab284-fcab284

Type of Work:

Article | Final Publisher PDF


Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal sclerosis. Identifying these MRI features is critical for the diagnosis and management of temporal lobe epilepsy. To date, this process relies on visual assessment by highly trained human experts (e.g. neuroradiologists, epileptologists). Artificial intelligence is increasingly recognized as a promising aid in the radiological evaluation of neurological diseases, yet its applications in temporal lobe epilepsy have been limited. Here, we applied a convolutional neural network to assess the classification accuracy of temporal lobe epilepsy based on structural MRI. We demonstrate that convoluted neural networks can achieve high accuracy in the identification of unilateral temporal lobe epilepsy cases even when the MRI had been originally interpreted as normal by experts. We show that accuracy can be potentiated by employing smoothed grey matter maps and a direct acyclic graphs approach. We further discuss the foundations for the development of computer-aided tools to assist with the diagnosis of epilepsy.

Copyright information:

© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/rdf).
Export to EndNote