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.
Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study / Gleichgerrcht, Ezequiel; Munsell, Brent C.; Alhusaini, Saud; Alvim, Marina K. M.; Bargallo, Nuria; Bender, Benjamin; Bernasconi, Andrea; Bernasconi, Neda; Bernhardt, Boris; Blackmon, Karen; Caligiuri, Maria Eugenia; Cendes, Fernando; Concha, Luis; Desmond, Patricia M.; Devinsky, Orrin; Doherty, Colin P.; Domin, Martin; Duncan, John S.; Focke, Niels K.; Gambardella, Antonio; Gong, Bo; Guerrini, Renzo; Hatton, Sean N.; Kalviainen, Reetta; Keller, Simon S.; Kochunov, Peter; Kotikalapudi, Raviteja; Kreilkamp, Barbara A. K.; Labate, Angelo; Langner, Soenke; Lariviere, Sara; Lenge, Matteo; Lui, Elaine; Martin, Pascal; Mascalchi, Mario; Meletti, Stefano; O'Brien, Terence J.; Pardoe, Heath R.; Pariente, Jose C.; Rao, Jun Xian; Richardson, Mark P.; Rodriguez-Cruces, Raul; Ruber, Theodor; Sinclair, Ben; Soltanian-Zadeh, Hamid; Stein, Dan J.; Striano, Pasquale; Taylor, Peter N.; Thomas, Rhys H.; Vaudano, Anna Elisabetta; Vivash, Lucy; von Podewills, Felix; Vos, Sjoerd B.; Weber, Bernd; Yao, Yi; Yasuda, Clarissa Lin; Zhang, Junsong; Thompson, Paul M.; Sisodiya, Sanjay M.; McDonald, Carrie R.; Bonilha, Leonardo. - In: JOURNAL OF NEUROIMAGING. - ISSN 1051-2284. - ELETTRONICO. - 31:(2021), pp. 0-0. [10.1016/j.nicl.2021.102765]
Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study
Guerrini, Renzo;Labate, Angelo;Lenge, Matteo;Mascalchi, Mario;
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 underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium.
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
