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Abstract Details

Assessment of Shared Neurologic Radiomic Markers Among Patients Diagnosed with Functional Seizure Disorder Utilizing a Novel Machine Learning Model
Aging, Dementia, and Behavioral Neurology
P6 - Poster Session 6 (5:30 PM-6:30 PM)
2-007
FSD is a diagnosis associated with significant disability, but that is ultimately poorly understood. Although some studies have suggested anatomic commonalities among FSD patients, consistent MRI markers and underlying anatomic abnormalities have yet to be elucidated. Clear demonstration of these factors would result in easier diagnosis and may even aid in developing novel treatments for the condition. As the literature has yet to conclusively identify MRI markers, a machine learning model trained through MRI segmentation offers a novel method to classify radiographic markers that would not be detectable through traditional analysis methods.
The objective of this study is to determine if a machine learning model can utilize magnetic resonance imaging (MRI) to differentiate patients with functional seizure disorder (FSD) from those with temporal lobe epilepsy (TLE).
After institutional review board approval, we retrospectively identified 64 patients with FSD and 23 with TLE who underwent workup in the epilepsy monitoring unit between 2015 and 2022. Patients were included in the study only if their typical episode was observed during EEG monitoring, thus confirming their diagnosis and study group. MRI for each patient was then pre-processed and systematically fed through a convolution neural network, generating a novel machine-learning model.
Although analysis and patient selection are still ongoing, our model has already identified several regions of interest. Currently, we have obtained a positive model certainty of 55% and a negative model certainty of 65% when training from the left thalamus, anterior corpus callosum, and left cerebral cortex.
Our findings demonstrate promise in using machine learning when assessing complex disorders such as functional seizure disorder. Furthermore, continued work with our machine-learning model may aid in identifying anatomic MRI markers shared by patients with FSD. This work may ultimately expand the understanding of the underlying mechanisms and diagnostic methods available for the disorder.
Authors/Disclosures
Jonah R. Moss, MD (Rush University Medical Center)
PRESENTER
Mr. Moss has nothing to disclose.
No disclosure on file
Adriana C. Bermeo Ovalle, MD (Rush University Medical Center) An immediate family member of Dr. Bermeo Ovalle has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Bioserenity. An immediate family member of Dr. Bermeo Ovalle has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Sunovion. An immediate family member of Dr. Bermeo Ovalle has received personal compensation in the range of $10,000-$49,999 for serving on a Speakers Bureau for Greenwich Biosciences. An immediate family member of Dr. Bermeo Ovalle has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for SK Life Science. An immediate family member of Dr. Bermeo Ovalle has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Neurelis. An immediate family member of Dr. Bermeo Ovalle has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Journal on Clinical Neuropysiology. An immediate family member of Dr. Bermeo Ovalle has received publishing royalties from a publication relating to health care.
No disclosure on file
Rebecca O'Dwyer, MD (Rush University Medical Center) Dr. O'Dwyer has nothing to disclose.