A machine learning algorithm that incorporates genetic data alongside clinical and demographic information may be able to more accurately predict the severity of multiple sclerosis (MS), according to a new study. “Once independently validated, the machine learning algorithm could enable clinicians to provide patients with more accurate prognostic information,…

A data-driven algorithm may be useful for defining the sometimes unclear transition from relapsing-remitting (RRMS) to secondary progressive (SPMS) forms of multiple sclerosis, a study found. The study, “Towards a validated definition of the clinical transition to secondary progressive multiple sclerosis: A study from the Italian MS Register,”…

A machine learning approach based on eye scans was employed by researchers to diagnose multiple sclerosis (MS) in children with up to 80% accuracy. Optical coherence tomography (OCT) scans also provided enough data to diagnose other demyelinating diseases with 75% accuracy. OCT is an imaging tool that uses…

Using a two-step machine learning strategy, researchers have developed an algorithm to predict the risk of multiple sclerosis (MS) relapse based on data gleaned from electronic health records. “The two-step machine learning model predicts a patient’s future one-year MS relapse risk with clinically actionable accuracy, comparable to other clinical…

A new machine learning algorithm — designed to analyze healthcare records — could help in diagnosing multiple sclerosis (MS) sooner by identifying patients’ symptoms earlier. The algorithm, devised by scientists at the University of California San Francisco (UCSF), was described in a study titled “Embedding electronic health records…

Artificial intelligence can help reduce the amount of gadolinium used as a contrast agent in MRI (magnetic resonance imaging) scans, preventing the agent’s potential toxicity when accumulated in body tissue, including the brain and bones, each time it is used, researchers suggest. The finding is of particular importance for…