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Abstract #1372

Differentiation of Multiple Sclerosis and Neuromyelitis Optica Spectrum Disorders by Convolutional Neural Network

Akifumi Hagiwara1,2, Yujiro Otsuka1,3, Christina Andica1, Shimpei Kato1,4, Kazumasa Yokoyama5, Masaaki Hori1,6, Shohei Fujita1,4, Koji Kamagata1, Ryusuke Irie1,4, Saori Koshino1,4, Tomoko Maekawa1,4, Toshiaki Akashi1, Akihiko Wada1, Kanako Kunishima Kumamaaru1, Takuya Haruyama1,7, Syo Murata1, Nobutaka Hattori5, and Shigeki Aoki1
1Radiology, Juntendo University School of Medicine, Tokyo, Japan, 2Radiology, UCLA, Los Angeles, CA, United States, 3Milliman Inc., Tokyo, Japan, 4Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, 5Neurology, Juntendo University School of Medicine, Tokyo, Japan, 6Radiology, Toho University, Tokyo, Japan, 7Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan

Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) are both neuroinflammatory diseases and have overlapping clinical manifestations. We developed a convolutional neural network that differentiates between MS and NMOSD based on multi-dynamic multi-echo sequence that measures R1 and R2 relaxation times and proton density. To avoid overfitting on a small dataset, we aimed to separate features of images into those specific to an image and those common to the group (i.e. MS or NMOSD) based on SqueezeNet. We used only common features for classification. Our model achieved a diagnostic accuracy of 80.7%.

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