Gleichgerrcht, Ezequiel, Munsell, Brent, Keller, Simon S ORCID: 0000-0001-5247-9795, Drane, Daniel L, Jensen, Jens H, Spampinato, M Vittoria, Pedersen, Nigel P, Weber, Bernd, Kuzniecky, Ruben, McDonald, Carrie et al (show 1 more authors)
(2022)
Radiological identification of temporal lobe epilepsy using artificial intelligence: a feasibility study.
Brain Communications, 4 (2).
fcab284-.
Abstract
<jats:title>Abstract</jats:title> <jats:p>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.</jats:p>
Item Type: | Article |
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Uncontrolled Keywords: | artificial intelligence, temporal lobe epilepsy, convoluted neural network, structural neuroimaging |
Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Systems, Molecular and Integrative Biology |
Depositing User: | Symplectic Admin |
Date Deposited: | 07 Mar 2022 08:34 |
Last Modified: | 18 Jan 2023 21:11 |
DOI: | 10.1093/braincomms/fcab284 |
Open Access URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC88879... |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/3150261 |