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dc.contributor.authorGleichgerrcht, Ezequiel
dc.contributor.authorMunsell, Brent C
dc.contributor.authorAlhusaini, Saud
dc.contributor.authorAlvim, Marina K M
dc.contributor.authorBargalló, Núria
dc.contributor.authorBender, Benjamin
dc.contributor.authorBernasconi, Andrea
dc.contributor.authorBernasconi, Neda
dc.contributor.authorBernhardt, Boris
dc.contributor.authorBlackmon, Karen
dc.contributor.authorCaligiuri, Maria Eugenia
dc.contributor.authorCendes, Fernando
dc.contributor.authorConcha, Luis
dc.contributor.authorDesmond, Patricia M
dc.contributor.authorDevinsky, Orrin
dc.contributor.authorDoherty, Colin P
dc.contributor.authorDomin, Martin
dc.contributor.authorDuncan, John S
dc.contributor.authorFocke, Niels K
dc.contributor.authorGambardella, Antonio
dc.contributor.authorGong, Bo
dc.contributor.authorGuerrini, Renzo
dc.contributor.authorHatton, Sean N
dc.contributor.authorKälviäinen, Reetta
dc.contributor.authorKeller, Simon S
dc.contributor.authorKochunov, Peter
dc.contributor.authorKotikalapudi, Raviteja
dc.contributor.authorKreilkamp, Barbara A K
dc.contributor.authorLabate, Angelo
dc.contributor.authorLangner, Soenke
dc.contributor.authorLarivière, Sara
dc.contributor.authorLenge, Matteo
dc.contributor.authorLui, Elaine
dc.contributor.authorMartin, Pascal
dc.contributor.authorMascalchi, Mario
dc.contributor.authorMeletti, Stefano
dc.contributor.authorO'Brien, Terence J
dc.contributor.authorPardoe, Heath R
dc.contributor.authorPariente, Jose C
dc.contributor.authorXian Rao, Jun
dc.contributor.authorRichardson, Mark P
dc.contributor.authorRodríguez-Cruces, Raúl
dc.contributor.authorRüber, Theodor
dc.contributor.authorSinclair, Ben
dc.contributor.authorSoltanian-Zadeh, Hamid
dc.contributor.authorStein, Dan J
dc.contributor.authorStriano, Pasquale
dc.contributor.authorTaylor, Peter N
dc.contributor.authorThomas, Rhys H
dc.contributor.authorElisabetta Vaudano, Anna
dc.contributor.authorVivash, Lucy
dc.contributor.authorvon Podewills, Felix
dc.contributor.authorVos, Sjoerd B
dc.contributor.authorWeber, Bernd
dc.contributor.authorYao, Yi
dc.contributor.authorLin Yasuda, Clarissa
dc.contributor.authorZhang, Junsong
dc.contributor.authorThompson, Paul M
dc.contributor.authorSisodiya, Sanjay M
dc.contributor.authorMcDonald, Carrie R
dc.contributor.authorBonilha, Leonardo
dc.date.accessioned2021-08-04T15:53:44Z
dc.date.available2021-08-04T15:53:44Z
dc.date.issued2021-07-24
dc.identifier.urihttp://hdl.handle.net/10713/16305
dc.description.abstractArtificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.en_US
dc.description.urihttps://doi.org/10.1016/j.nicl.2021.102765en_US
dc.language.isoenen_US
dc.publisherElsevier Inc.en_US
dc.relation.ispartofNeuroImage. Clinicalen_US
dc.rightsCopyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.en_US
dc.subjectArtificial inteligenceen_US
dc.subjectEpilepsyen_US
dc.subjectMachine learningen_US
dc.subjectTemporal lobe epilepsyen_US
dc.titleArtificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy studyen_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.nicl.2021.102765
dc.identifier.pmid34339947
dc.source.volume31
dc.source.beginpage102765
dc.source.endpage
dc.source.countryNetherlands


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