To propose a new fully automated method to rate neurological examination in multiple sclerosis, which would be more linear, reliable and minimize the risk of rating errors.

Developing innovative clinical assessment tools in MS is a significant unmet need as EDSS suffers from non-linearity, rating complexity, and inter-rater variability

We developed a mobile application in which the user reports the neurological examination. The app contains an algorithm that associates each neurological sign to one of the eight EDSS functional parameters (FP) and automatically calculates a score (mobile FP, mFP) between 0 and 100 for each of them (100 corresponding to a normal examination).

We tested the app on a cohort of patients with MS addressed in our tertiary center, and compared mFP scores with traditional EDSS FP scores. 

We included 62 patients in our study: mean age: 43.7yrs, F/M ratio: 2.1, EDSS: 3 [0-7.5].

For each FP, we found a strong correlation (p<0.01) with the corresponding mFP: pyramidal (r=-0.94), sensory (r=-0.66), cerebellar (r=-0.69), cognition (r=-0.78), brainstem (r=-0.85), bowel and bladder (r=-0.96) and vision (r=-0.89).

The Kruskal-Wallis test confirmed that mFP median values were significantly different according to traditional FP values for all EDSS FP (p<0.01). 

Our algorithm constitutes a more linear, less subjective, and automatic neurological exam rating method in MS while remaining strongly correlated to EDSS FP.

It could be used to monitor disability progression more accurately as it considers more clinical manifestations such as fatigue, neuropathic pain or cognitive impairment that are under represented in EDSS. 

We present an innovative, automated method to rate neurological examination in MS that only requires the user to describe neurological findings. 

We now plan to assess its sensitivity to change and inter-rater variability