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Abstract #0849

Novel MRI sequence-agnostic deep learning model to localize seizure onset zone in pediatric drug-resistance epilepsy

Jeong-Won Jeong1, Soumyanil Banerjee2, Min-Hee Lee3, Ming Dong2, Eishi Asano1, and Csaba Juhasz1
1Pediatrics, Neurology, and Translational Neuroscience, Wayne State University, Detroit, MI, United States, 2Computer Science, Wayne State University, Detroit, MI, United States, 3Pediatrics, Wayne State University, Detroit, MI, United States

Synopsis

Keywords: Neuro, Epilepsy, Drug-resistant epilepsy, seizure onset zone, clinical MRI

Motivation: To solve an incomplete sequence problem in localizing seizure onset zone (SOZ) using clinical MRI of children with drug-resistant epilepsy,

Goal(s): We develop a sequence-agnostic deep learning approach that can localize SOZ even using subsets of clinical MRI sequence data.

Approach: This consisted of 1) a sequence-agnostic model with cross-sequence distillation to train sequence specific and shared feature extractors for each sequence data and 2) a shared classification head to localize SOZ sites using a specific subset of multiple sequence data.

Results: Our approach provided a high accuracy of 96%/89%/86% in classifying SOZ sites using five/four/three MRI sequence data of a validation cohort.

Impact: Sequence-agnostic learning approach could accurately classify seizure onset zone (SOZ) using clinical MRI sequence data of pediatric drug-resistant epilepsy. It provided a new way to better generalize the SOZ classification in multi-center studies that suffer from missing sequence data problem

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