The role of AI for MRI-analysis in multiple sclerosis—A brief overview - Jean-Pierre R. Falet; Jean-Pierre R. Falet; Jean-Pierre R. Falet; Steven Nobile; Aliya Szpindel; Berardino Barile; Berardino Barile; Amar Kumar; Amar Kumar; Joshua Durso-Finley; Joshua Durso-Finley; Tal Arbel; et al. (Frontiers in Artificial Intelligence)
Magnetic resonance imaging (MRI) has played a crucial role in the diagnosis, monitoring and treatment optimization of multiple sclerosis (MS). It is an essential component of current diagnostic criteria for its ability to non-invasively visualize both lesional and non-lesional pathology. Nevertheless, modern day usage of MRI in the clinic is limited by lengthy protocols, error-prone procedures for identifying disease markers (e.g., lesions), and the limited predictive value of existing imaging biomarkers for key disability outcomes. Recent advances in artificial intelligence (AI) have underscored the potential for AI to not only improve, but also transform how MRI is being used in MS. In this short review, we explore the role of AI in MS applications that span the entire life-cycle of an MRI image, from data collection, to lesion segmentation, detection, and volumetry, and finally to downstream clinical and scientific tasks. We conclude with a discussion on promising future directions.