Abstract #0024

Deep Learning for Identification of Active Lesions in Multiple Sclerosis Without Administration of Gadolinium Based Contrast Agent

Ponnada A Narayana¹, Ivan Coronado¹, Sushmita Datta¹, Sheeba J Sujit¹, Fred D Lublin², Jerry S Wolinsky³, and Refaat E Gabr¹

¹Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston, Houston, TX, United States, ²Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³Neurology, University of Texas Health Science Center at Houston, Houston, TX, United States

Gadolinium based contrast agents (GBCA) are routinely administered for identifying active lesions in multiple sclerosis (MS). Because of the safety concerns with GBCA, alternative methods are highly desirable to identify active lesions without GBCA. We used Deep Learning, specifically multi-layered VGG16 network, to identify active MS lesions on the pre-contrast images. The network was trained using a large number of annotated multi-modal magnetic resonance image volumes (792) acquired as a part of phase 3 clinical. The DL results look quite promising as judged by the accuracy, sensitivity, and specificity of 0.729, 0.861, 0.598, respectively.

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