About Us

More than 2.8 million people worldwide and 1.2 million in the EU live with multiple sclerosis (MS). MS affects young adults, causing significant disability early in life. Clinicians define MS categories based on the clinical course. However, the clinical course does not relate to underlying causes, creating a mismatch between the diagnosed categories and the disease mechanisms. As a consequence of this mismatch, treatments, which are selected based on symptoms and disease course, may not be effective because they do not target underlying mechanisms. Measurable disease markers, such as those extracted from brain images or blood, reflect the underpinning disease biology.

 

With the availability of large data sets and artificial intelligence (AI), we can now recognise patterns related to disease subtypes that were once impossible to detect. At the intersection of clinical neurology and computer science, QSA aims to redefine how MS subtypes are classified using AI applied to quantifiable measures (imaging and blood) that reflect the disease biology more directly than clinical symptoms. Data-driven subtypes are then matched with treatments that target the identified biological changes, ultimately enabling the selection of effective therapies. Because changes in brain images and blood biomarkers precede clinical deterioration, staging patients in data-driven subtypes will predict disability worsening. QSA has access to large data sets from clinical trials, observational cohorts and real-world clinical data that have enabled the development of technology which defines data-driven MS subtypes in adults and children. This innovation provides a revolutionary computational framework that will use routinely available data generated at the point of care while preserving patient privacy to subtype MS patients. It will improve how patients are selected into clinical trials and will allow clinicians to choose the best therapy for each patient.