Differentiation of Acute Disseminated Encephalomyelitis from Multiple Sclerosis Using a Novel Brain Lesion Segmentation and Classification Pipeline

Abstract

Multiple Sclerosis (MS) is a chronic autoimmune disease affecting the central nervous system, while Acute Disseminated Encephalomyelitis (ADEM) is a sudden, often monophasic inflammatory condition of the brain and spinal cord. Only 17% of ADEM cases are correctly diagnosed on the first visit due to overlapping clinical and radiological presentations with Multiple Sclerosis (MS) [1]. Both ADEM and MS are demyelinating diseases, meaning they cause brain lesions by damaging the myelin sheath, leading to scar tissue that disrupts nerve signals [2]. Previous machine learning pipelines have differentiated Neuromyelitis Optica Spectrum Disorder (NMOSD) (a different demyelinating disease) from MS and ADEM from NMOSD based on MRI imagery with varying accuracies [3, 4]. Our novel Classifier for Demyelinating Disease (CDD) pipeline is the first to differentiate ADEM from MS using MRI imagery. It does this in two stages: a segmentation stage which creates segmentation masks of the lesions and a classification stage to classify them as either ADEM or MS. Additionally, we introduce a novel ADEM dataset from open-access medical case reports. The CDD pipeline achieves an accuracy of 90.0% on our validation dataset, making it a potentially viable diagnostic tool in the future. All data and code is available here. 2