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

• Authors: Ezequiel Gleichgerrcht, Medical University of South Carolina; Brent Munsell, University of North Carolina, Chapel Hill; Simon S Keller,, University of Liverpool; Daniel Drane, Emory University; Jens H Jensen, Medical University of South Carolina; Vittoria M Spampinato, Department of Radiology, Medical University of South Carolina, Charleston, SC 29425, USA.; Nigel Pedersen, Emory University; Bernd Weber, Institute of Experimental Epileptology and Cognition Research, University of Bonn, Bonn 53113, Germany.; Ruben Kuzniecky, Hofstra University/Northwell, New York; Carrie McDonald, University of California San Diego, La Jolla; Leonardo Bonilha, Emory University
• Journal Title: Brain Commun
• Volume: Volume 4, Number 2
• Publisher: Oxford University Press | 2022, Pages fcab284-fcab284
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1093/braincomms/fcab284
• Subjects: Health Sciences, Mental Health; Engineering, Biomedical; Health Sciences, Radiology
• Keywords: artificial intelligence; convoluted neural network; structural neuroimaging; temporal lobe epilepsy

Abstract

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.