Abstract #3378
Identification of Subcortical White Matter Biomarkers in Multiple Sclerosis Patients using Machine Learning
Cristian Montalba1,2,3, Raul Caulier-Cisterna1,3, Pamela Franco1,3, Tomás Labbé2, Marcelo E Andia1,2,3, Miguel Guevara4, Jean-François Mangin5, Juan Pablo Cruz2, Ethel Ciampi6,7, Claudia Cárcamo7,8, Pamela Guevara4, Rodrigo Salas3,9,10, and Sergio Uribe1,2,3
1Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Radiology Department, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Pontificia Universidad Catolica de Chile, Santiago, Chile, 4Faculty of Engineering, Universidad de Concepción, Concepción, Chile, 5UNATI, Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France, 6Neurology Service, Hospital Dr. Sótero del Río, Santiago, Chile, 7Neurology Department, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile, 8Interdisciplinary Center of Neurosciences, Pontificia Universidad Catolica de Chile, Santiago, Chile, 9Faculty of Engineering, Universidad de Valparaíso, Valparaíso, Chile, 10Biomedical Engineering School, Universidad de Valparaíso, Valparaíso, Chile
Synopsis
Keywords: Multiple Sclerosis, Machine Learning/Artificial IntelligenceRadiological biomarkers of cognitive impairment in Multiple Sclerosis (MS) are still scarce. This study aimed to identify subcortical white matter biomarkers of cognitive impairment related to verbal episodic memory in MS patients and healthy controls using a Machine Learning approach.
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