Validated, Quantitative, Machine Learning-Generated Neurologic Assessment of Multiple Sclerosis Using a Mobile Application

Abstract

Abstract Background: The BeCare MS Link mobile app collects data as users complete different in-app assessments. It was specifically developed to evaluate the symptomatology and neurologic function of patients with multiple sclerosis (MS) and to become a digital equivalent of the Expanded Disability Status Scale (EDSS) and other standard clinical metrics of MS progression. Methods: Our research compared EDSS scores derived from the BeCare MS link app to EDSS scores derived from neurologist assessment for the same cohort of 35 patients diagnosed with MS. App-derived data was supplied to 4 different machine learning algorithms (MLAs) with an independent EDSS score prediction generated from each. These scores were compared to the clinically-derived EDSS score to assess the similarity of the scores and to determine an accuracy estimate for each MLA. The trial is registered on ClinicalTrials.gov as NCT04281160. Results: Out of the 4 MLAs employed, the most accurate MLA produced 19 EDSS score predictions that exactly matched the clinically-derived scores, 21 score predictions within 0.5 EDSS points, and 32 score predictions within 1 EDSS point. The remaining MLAs also provided a relatively high level of accuracy in predicting EDSS scores when compared to clinically-derived EDSS, with over 80% of scores predicted within 1 point and a mean squared error with a range of 1.05 to 1.37. Conclusions: The BeCare MS Link app can replicate the clinically-derived EDSS assessment of a patient with MS. The app may also offer a more complete evaluation of disability in patients with MS.