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.

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study / Gleichgerrcht, E.; Munsell, B. C.; Alhusaini, S.; Alvim, M. K. M.; Bargallo, N.; Bender, B.; Bernasconi, A.; Bernasconi, N.; Bernhardt, B.; Blackmon, K.; Caligiuri, M. E.; Cendes, F.; Concha, L.; Desmond, P. M.; Devinsky, O.; Doherty, C. P.; Domin, M.; Duncan, J. S.; Focke, N. K.; Gambardella, A.; Gong, B.; Guerrini, R.; Hatton, S. N.; Kalviainen, R.; Keller, S. S.; Kochunov, P.; Kotikalapudi, R.; Kreilkamp, B. A. K.; Labate, A.; Langner, S.; Lariviere, S.; Lenge, M.; Lui, E.; Martin, P.; Mascalchi, M.; Meletti, S.; O'Brien, T. J.; Pardoe, H. R.; Pariente, J. C.; Xian Rao, J.; Richardson, M. P.; Rodriguez-Cruces, R.; Ruber, T.; Sinclair, B.; Soltanian-Zadeh, H.; Stein, D. J.; Striano, P.; Taylor, P. N.; Thomas, R. H.; Vaudano, A.; Vivash, L.; von Podewills, F.; Vos, S. B.; Weber, B.; Yao, Y.; Lin Yasuda, C.; Zhang, J.; Thompson, P. M.; Sisodiya, S. M.; Mcdonald, C. R.; Bonilha, L.; Altmann, A.; Depondt, C.; Galovic, M.; Thomopoulos, S. I.; Wiest, R.. - In: NEUROIMAGE. CLINICAL. - ISSN 2213-1582. - 31:(2021), pp. N/A-N/A. [10.1016/j.nicl.2021.102765]

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study

Meletti S.;Vaudano A.;
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. 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.
2021
31
N/A
N/A
Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study / Gleichgerrcht, E.; Munsell, B. C.; Alhusaini, S.; Alvim, M. K. M.; Bargallo, N.; Bender, B.; Bernasconi, A.; Bernasconi, N.; Bernhardt, B.; Blackmon, K.; Caligiuri, M. E.; Cendes, F.; Concha, L.; Desmond, P. M.; Devinsky, O.; Doherty, C. P.; Domin, M.; Duncan, J. S.; Focke, N. K.; Gambardella, A.; Gong, B.; Guerrini, R.; Hatton, S. N.; Kalviainen, R.; Keller, S. S.; Kochunov, P.; Kotikalapudi, R.; Kreilkamp, B. A. K.; Labate, A.; Langner, S.; Lariviere, S.; Lenge, M.; Lui, E.; Martin, P.; Mascalchi, M.; Meletti, S.; O'Brien, T. J.; Pardoe, H. R.; Pariente, J. C.; Xian Rao, J.; Richardson, M. P.; Rodriguez-Cruces, R.; Ruber, T.; Sinclair, B.; Soltanian-Zadeh, H.; Stein, D. J.; Striano, P.; Taylor, P. N.; Thomas, R. H.; Vaudano, A.; Vivash, L.; von Podewills, F.; Vos, S. B.; Weber, B.; Yao, Y.; Lin Yasuda, C.; Zhang, J.; Thompson, P. M.; Sisodiya, S. M.; Mcdonald, C. R.; Bonilha, L.; Altmann, A.; Depondt, C.; Galovic, M.; Thomopoulos, S. I.; Wiest, R.. - In: NEUROIMAGE. CLINICAL. - ISSN 2213-1582. - 31:(2021), pp. N/A-N/A. [10.1016/j.nicl.2021.102765]
Gleichgerrcht, E.; Munsell, B. C.; Alhusaini, S.; Alvim, M. K. M.; Bargallo, N.; Bender, B.; Bernasconi, A.; Bernasconi, N.; Bernhardt, B.; Blackmon, K.; Caligiuri, M. E.; Cendes, F.; Concha, L.; Desmond, P. M.; Devinsky, O.; Doherty, C. P.; Domin, M.; Duncan, J. S.; Focke, N. K.; Gambardella, A.; Gong, B.; Guerrini, R.; Hatton, S. N.; Kalviainen, R.; Keller, S. S.; Kochunov, P.; Kotikalapudi, R.; Kreilkamp, B. A. K.; Labate, A.; Langner, S.; Lariviere, S.; Lenge, M.; Lui, E.; Martin, P.; Mascalchi, M.; Meletti, S.; O'Brien, T. J.; Pardoe, H. R.; Pariente, J. C.; Xian Rao, J.; Richardson, M. P.; Rodriguez-Cruces, R.; Ruber, T.; Sinclair, B.; Soltanian-Zadeh, H.; Stein, D. J.; Striano, P.; Taylor, P. N.; Thomas, R. H.; Vaudano, A.; Vivash, L.; von Podewills, F.; Vos, S. B.; Weber, B.; Yao, Y.; Lin Yasuda, C.; Zhang, J.; Thompson, P. M.; Sisodiya, S. M.; Mcdonald, C. R.; Bonilha, L.; Altmann, A.; Depondt, C.; Galovic, M.; Thomopoulos, S. I.; Wiest, R.
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