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Multiple sclerosis is the most common disabling neurologic disease affecting young adults and adolescents in the United States. The first objective of this article is to familiarize nonspecialists with the cardinal features of multiple sclerosis and our current understanding of its etiology, epidemiology, and natural history. The second objective is to explain the approach to diagnosis. The third is to clarify current evidence-based treatment strategies and their roles in disease modification. The overall goal is to facilitate the timely evaluation and confirmation of diagnosis and enhance effective management through collaboration among primary physicians, neurologists, and other care providers who are confronted with these formidably challenging patients.

Multiple sclerosis is the most common disabling neurological disease of young adults. The ability to impact the quality of life of patients with multiple sclerosis should not only incorporate therapies that are disease modifying, but should also include a course of action for the global multidisciplinary management focused on quality of life and functional capabilities.

In the absence of treatments to reverse neurologic injury due to MS, effective symptom management and functional improvement remain essential to mitigate disability and maintain quality of life. Basic research, as well as controlled clinical trials, in this realm offers promising insights and solutions.

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

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Multiple Sclerosis (MS) is the leading disease of the central nervous system and a significant, costly cause of disability in young adults. Patients at risk for MS often present with a single demyelinating event, typically called a “clinically isolated syndrome” (CIS). A CIS can occur in the brain, spinal cord (Acute Partial Transverse Myelitis, APTM), or optic nerve (Optic Neuritis, ON) at similar rates and present a challenging diagnostic and therapeutic dilemma. Stratifying CIS patients most likely to develop MS is a complicated process, but desirable since treatment with the appropriate immunomodulatory agents early in the disease course can delay long-term disability. Thus, a primary focus of our laboratory has been to develop a completely novel type of biomarker that can identify CIS patients that will develop MS. This unique biomarker is based on a pattern of antibody gene mutations (i.e. antibody gene signature or “AGS”) that is exclusive to B cells from the cerebrospinal fluid and brain lesions of MS patients that initially presented with either TM or ON. In fact, the AGS is consistently elevated in patients with one attack of ON that go on to develop definite MS, and can predict conversion to MS with 91% accuracy. More recently, we are testing the hypothesis that the AGS serves as a predictor for conversion to MS in CIS patients that present with TM. Preliminary data demonstrate that the AGS is also prevalent in CIS patients presenting with TM.