Kimberly Jin Peter Kosa Bibiana Bielekova ^*

• National Institutes of Health (NIH), Bethesda, Maryland, United States

Increasing shortage of neurologists compounded by the global aging of the population have translated into suboptimal care of patients with chronic neurological diseases. While some patients might benefit from expanding telemedicine, monitoring neurological disability via telemedicine is challenging. Smartphone technologies represents an attractive tool for remote, selfadministered neurological assessment. To address this need, we have developed a suite of smartphone tests, called Neurological Functional Test Suite (NeuFun-TS), designed to replicate traditional neurological examination. The aim of this study was to assess the ability of two NeuFun-TS tests -Short Walk and Foot Tapping -to quantify motor functions of lower extremities as assessed by a neurologist.A cohort of 108 multiple sclerosis (MS) patients received a full neurological examination, imaging of the brain, and completed the NeuFun-TS smartphone tests. The neurological exam was digitalized using the NeurEx TM platform, providing calculation of traditional disability scales, as well as quantification of lower extremities-specific disability. We assessed unilateral correlations of 28 digital biomarkers generated from the NeuFun-TS tests with disability and MRI outcomes and developed machine-learning models that predict physical disability. Model performance was tested in an independent validation cohort.Results: NeuFun-TS-derived digital biomarkers correlated strongly with traditional outcomes related to gait and lower extremities functions (e.g., Spearman Rho > 0.8). As expected, the correlation with global disability outcomes was weaker, but still highly significant (e.g. Rho 0.46-0.65; p<0.001 for EDSS). Digital biomarkers also correlated with semi-quantitative imaging outcomes capturing locations that can affect lower extremity functions (e.g, Rho ~ 0.4 for atrophy of medulla). Reliable digital outcomes with high test-retest values showed stronger correlation with disability outcomes. Combining strong, reliable digital features using machine learning resulted in models that outperformed predictive power of best individual digital biomarkers in an independent validation cohort.Discussion: NeuFun-TS tests provide reliable digital biomarkers of lower extremity motor functions.