Journal article
Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study
Ezequiel Gleichgerrcht, Brent C Munsell, Saud Alhusaini, Marina KM Alvim, Nuria Bargallo, Benjamin Bender, Andrea Bernasconi, Neda Bernasconi, Boris Bernhardt, Karen Blackmon, Maria Eugenia Caligiuri, Fernando Cendes, Luis Concha, Patricia M Desmond, Orrin Devinsky, Colin P Doherty, Martin Domin, John S Duncan, Niels K Focke, Antonio Gambardella Show all
NEUROIMAGE-CLINICAL | ELSEVIER SCI LTD | Published : 2021
Abstract
Artificial 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 underl..
View full abstractGrants
Awarded by NINDS
Awarded by FAPESP
Awarded by SickKids Foundation
Awarded by Mexican Council of Science and Technology
Awarded by UNAM-DGAPA
Awarded by DFG
Awarded by Medical Research Council
Awarded by Epilepsy Research UK
Awarded by PATE program
Awarded by Italian Ministry of Health
Awarded by Medical Research Council Centre for Neurodevelopmental Disorders
Awarded by Fonds de recherche du Quebec - Sante
Awarded by DINOGMI Department of Excellence of MIUR
Awarded by CNPQ
Awarded by National Nature Science Foundation of China
Awarded by NIH
Awarded by NIH Big Data to Knowledge (BD2K) program under consortium
Awarded by Swiss National Science Foundation
Awarded by Epilepsy Society - FAPESP (Sao Paulo Research Foundation)
Funding Acknowledgements
Funding and acknowledgements B.C.M. is supported by NINDS R21 NS107739-01A1. M.K.M.A. is supported by FAPESP 15/17066-0. A.B. is supported by CIHR MOP-57840. N.Be. is supported by CIHR MOP-123520; CIHR MOP-130516. B.Ber. acknowledges research support from NSERC (Discovery-1304413) , CIHR (FDN-154298) , Azrieli Center for Autism Research of the Montreal Neurological Institute (ACAR) , SickKids Foundation (NI17-039) , and salary support from FRQS (Chercheur Boursier Junior 1) . L.C. is supported by Mexican Council of Science and Technology (CONACYT 181508, 232676, 251216, and 280283) ; UNAM-DGAPA (IB201712) . O. D. is supported by Finding A Cure for Epilepsy and Seizures (FACES) . J.S. D. is supported by NIHR. N.K.F. is supported by DFG FO750/5-1. R.Kad. is supported by Saas-tamoinen Foundation. S.S.K. is supported by Medical Research Council (MR/S00355X/1 and MR/K023152/1) and Epilepsy Research UK (1085) . P.K. is supported by S10OD023696; R01EB015611. S.La. is funded by CIHR. P.M. was supported by the PATE program (F1315030) of the University of Tubingen. S.M. is supported by Italian Ministry of Health funding grant NET-2013-02355313. T.J. is supported by NHMRC Program Grant. M.R. is supported by Medical Research Council programme grant (MR/K013998/1) ; Medical Research Council Centre for Neurodevelopmental Disorders (MR/N026063/1) ; NIHR Biomedical Research Centre at South London and Maudsely NHS Foundation Trust. R.R.-C. is supported by the Fonds de recherche du Quebec - Sante (FRQS-291486) . D.J.S. is supported by SA Medical Research Council. Work developed within the framework of the DINOGMI Department of Excellence of MIUR 2018-2022 (legge 232 del 2016) . R.H.T. is supported by Epilepsy Research UK. C.L.Y. is supported by FAPESP-BRAINN (2013/07599-3) ; CNPQ (403726/2016-6) . J.S.Z. is supported by National Nature Science Foundation of China (No. 61772440) . C.R.M. is supported by NIH R01 NS065838; R21 NS107739. L.B. is supported by R01NS110347 (NIH/NINDS) . A.A. holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship; this work was partly supported by the Medical Research Council [grant number MR/L016311/1] . R.W. received support from the Swiss League Against Epilepsy. Core funding for ENIGMA was provided by the NIH Big Data to Knowledge (BD2K) program under consortium grant U54 EB020403. The Bern research centre was funded by Swiss National Science Foundation (grant 180365) . This work was partly undertaken at UCLH/UCL, which received a proportion of funding from theDepartment of Health's NIHR Biomedical Research Centres funding scheme. The work was also supported by the Epilepsy Society, UK. We are grateful to the Wolfson Trust and the Epilepsy Society for supporting the Epilepsy Society MRI scanner. The UNICAMP research centre was funded by FAPESP (Sao Paulo Research Foundation) ; Contract grant number: 2013/07559-3. Lastly, we are grateful for software develop-ment and high-performance computing work performed by UNC Chapel Hill graduate research assistant Kyuyeon Kim.