Machine Learning-Based Classification of Acute versus Chronic Multiple Sclerosis Lesions using Radiomic Features from Unenhanced Cross-Sectional Brain MRI (4121)
Bastien Caba, Dawei Liu(Biogen (United States)), Aurélien Lombard, Natasha Novikov(Biogen (United States)), Alexandre Cafaro, Daniel P. Bradley(Biogen (United States)), Enzo Battistella(Université Paris-Saclay), Elizabeth Fisher(Biogen (United States)), Nathalie Franchimont(Biogen (United States)), Arie Gafson(Biogen (United States)), Parya MomayyezSiahkal(NeuroRx Research (Canada)), Zahra Karimaghaloo(McGill University), Douglas L. Arnold(NeuroRx Research (Canada)), Colm Elliott(McGill University), Nikos Paragios(CentraleSupélec), Shibeshih Belachew(Biogen (United States))
Cited by 2
Abstract
To build a machine learning/artificial intelligence-based (ML/AI) tool to classify acute (T1 gadolinium-enhancing [T1Gd+] or new T2 hyperintense lesions) versus chronic T2 hyperintense multiple sclerosis (MS) lesions using only cross-sectional T1- and T2-weighted brain MRI without gadolinium contrast information.
Related Papers
No related papers found
Powered by citation graph analysis