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Abstract #4883

Differential diagnosis of multiple sclerosis based on the central vein sign assessment using deep learning: a multicentre study.

Mário João Fartaria1,2,3, Jonas Richiardi1,2,3, Pietro Maggi4, Pascal Sati5, Daniel S. Reich5, Cristina Granziera6,7, Meritxell Bach Cuadra2,3,8, and Tobias Kober1,2,3

1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 3Signal Processing Laboratory (LTS 5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 4Departement of Neurology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 5Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), Bethesda, MD, United States, 6Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 7Translational Imaging in Neurology (ThINk) Basel, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland, 8Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland

Prospective multicentre studies are needed to establish the clinical value of the central vein sign for diagnosis of multiple sclerosis. This type of studies requires manual segmentation and classification of lesions with and without the central vein sign, which are time-consuming tasks. In this work, we evaluate the performance of an in-house deep-learning-based prototype algorithm for automated assessment of the central vein sign using data from two different healthcare units.

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