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.
Highlights
Temporal lobe epilepsy (TLE) is the most common focal onset epi lepsy in adults (Tellez-Zenteno and Hernandez-Ronquillo, 2012)
There was a 73 ± 2.9% (DL) to 75 ± 3.4% (SVM) accuracy to correctly distinguish all patients with temporal lobe epilepsy (TLE) from HC based on grey matter (GM) volumes/thickness, the accuracy was slightly lower (65–67%) when the model attempted to classify TLE patients based on the specific side of presumed seizure onset (i.e., TLE-HS-L and TLE-HS-R, separately) from HC
Artificial intelligence might allow for the classification and lateral izing of TLE using region of interest (ROI)-level data, with moderate accuracy
Summary
Temporal lobe epilepsy (TLE) is the most common focal onset epi lepsy in adults (Tellez-Zenteno and Hernandez-Ronquillo, 2012). Hippocampal sclerosis is a common pathological finding in TLE (Babb and Brown, 1987; Blumcke et al, 2017), structural abnor malities in TLE are not restricted to the medial temporal region and may extend to the neocortex (Blanc et al, 2011; Margerison and Corsellis, 1966). Tissue volume abnormalities occur both proximal and distal to medial temporal lobe circuits with histopathologically confirmed hippocampal sclerosis and EEG confirmation of concordant seizure laterality (Ahmadi et al, 2009; Bernhardt et al, 2010; Bernhardt et al, 2008; Bonilha et al, 2010a; Bonilha et al, 2010b; Bonilha et al, 2003; Caligiuri et al, 2016; Focke et al, 2008; Lin et al, 2007; McDonald et al, 2008b). Methods that can accurately detect, quantify, and profile structural abnormalities at an individual level can be relevant for multiple aspects of translational epilepsy research and may help guide clinical management in the future (Gleichgerrcht and Bonilha, 2017; Gleichgerrcht et al, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
