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Abstract #1930

Application of Machine Learning in Comparison between Multimodal Neuroimaging Markers of Laterality in Temporal Lobe Epilepsy

Alireza Fallahi 1,2, Mohammad Pooyan3, Jafar Mehvari-Habibabadi 4, Narges Hoseini Tabatabaei5, Mohammadreza Ay1,6, and Mohammad-Reza Nazem-Zadeh1,6
1Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 2Biomedical Engineering, Hamedan University of Technology, Hamedan, Iran (Islamic Republic of), 3Biomedical Engineering, Shahed University, Tehran, Iran (Islamic Republic of), 4Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Iran (Islamic Republic of), 5Medical School, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), 6Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)


Five neuroimaging markers including T1 volume, FLAIR signal intensity, and mean diffusivity in hippocampus, and fractional anisotropy in both posteroinferior cingulum and crus of fornix were used for lateralization of temporal lobe epilepsy (TLE). Support vector machine (SVM) was used as a classifier and for measuring the importance of neuroimaging attributes. The classification results demonstrated that the hippocampal volumetric and mean diffusivity showed the highest correct classification rate and the largest area under the curve (AUC) for the receiver operating characteristic (ROC), thus considered as the most important attributes of TLE laterality among all markers investigated in this study.

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