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Abstract #4871

Diagnosis of Multiple Sclerosis Subtype through Machine Learning Analysis of Frontal Cortex Metabolite Profiles

Abhinav V. Kurada1, Kelley M. Swanberg1,2, Hetty Prinsen2, and Christoph Juchem1,2,3,4

1Biomedical Engineering, Columbia University School of Engineering and Applied Science, New York, NY, United States, 2Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States, 3Neurology, Yale University School of Medicine, New Haven, CT, United States, 4Radiology, Columbia University Medical Center, New York, NY, United States

The onset and progression of multiple sclerosis (MS) is accompanied by changes in brain biochemistry. Magnetic resonance spectroscopy (MRS) is a powerful tool for investigating these changes in vivo. Machine learning analysis of MRS-derived biochemical profiles may reveal metabolic patterns inherent in certain MS subtypes to inform their diagnosis. By employing a feature set of only metabolite concentrations derived from brain MRS data acquired at 7 Tesla, we achieved an 80% validation set accuracy for differentiating MS patients from healthy controls and a 70% validation set accuracy for differentiating relapsing-remitting and progressive MS patients.

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