Abstract #4883

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

Mário João Fartaria^1,2,3, Jonas Richiardi^1,2,3, Pietro Maggi⁴, Pascal Sati⁵, Daniel S. Reich⁵, Cristina Granziera^6,7, Meritxell Bach Cuadra^2,3,8, and Tobias Kober^1,2,3

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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