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 K M Alvim Núria Bargalló 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 Bo Gong Renzo Guerrini Sean N HattonReetta KälviäinenSimon S Keller Peter Kochunov Raviteja Kotikalapudi Barbara A K Kreilkamp Angelo Labate Soenke Langner Sara Larivière Matteo Lenge Elaine Lui Pascal Martin Mario Mascalchi Stefano Meletti Terence J O’Brien,  Heath R Pardoe Jose C Pariente Jun Xian Rao Mark P Richardson Raúl Rodríguez-CrucesTheodor Rüber Ben Sinclair Hamid Soltanian-Zadeh Dan J Stein Pasquale Striano Peter N Taylor Rhys H Thomas Anna Elisabetta Vaudano Lucy Vivash Felix von Podewills Sjoerd B Vos Bernd Weber Yi Yao Clarissa Lin Yasuda, Junsong Zhang Paul M Thompson , Sanjay M Sisodiya Carrie R McDonald Leonardo Bonilha ENIGMA-Epilepsy Working Group


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 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.

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