Deep learning in neuroimaging of epilepsy.

Autor: García-Ramó KB; Group of Neuroimaging Processing, International Center for Neurological Restoration, Cuba; Department of Clinical Investigations, Center of Isotopes, Cuba. Electronic address: karlabg@infomed.sld.cu., Sanchez-Catasus CA; Department of Neurology, Clínica Universidad de Navarra, Spain; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, the Netherlands. Electronic address: casanchezcatasus@gmail.com., Winston GP; Division of Neurology, Department of Medicine, Queen's University, Canada; Centre for Neuroscience Studies, Queen's University, Canada. Electronic address: gavin.winston@queensu.ca.
Jazyk: angličtina
Zdroj: Clinical neurology and neurosurgery [Clin Neurol Neurosurg] 2023 Sep; Vol. 232, pp. 107879. Date of Electronic Publication: 2023 Jul 06.
DOI: 10.1016/j.clineuro.2023.107879
Abstrakt: In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional machine learning. It is helpful for the assessment of patients with epilepsy and whilst most published studies have been aimed at the automatic detection and prediction of seizures from electroencephalographic records, there is a growing number of investigations that use neuroimaging modalities (structural and functional magnetic resonance imaging, diffusion-weighted imaging and positron emission tomography) as input data. We review the application of DL to neuroimaging (sMRI, fMRI, DWI and PET) of focal epilepsy, specifically presurgical evaluation of drug-refractory epilepsy. First, a brief theoretical overview of artificial neural networks and deep learning is presented. Next, we review applications of deep learning to neuroimaging of epilepsy: diagnosis and lateralization, automated detection of lesion, presurgical evaluation and prediction of postsurgical outcome. Finally, the limitations, challenges and possible future directions in the application of these methods in the study of epilepsies are discussed. This approach could become an essential tool in clinical practice, particularly in the evaluation of images considered negative by visual inspection, in individualized treatments, and in the approach to epilepsy as a network disorder. However, greater multicenter collaboration is required to achieve the collection of sufficient data with the required quality together with the open access availability of the developed codes and tools.
Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest.
(Copyright © 2023 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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