ArticleInternational journal of MS care
Validated, Quantitative, Machine Learning-Generated Neurologic Assessment of Multiple Sclerosis Using a Mobile Application.
Article in International journal of MS care. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- From lesion detection to outcome prediction: artificial intelligence and deep learning applications in multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsOur 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 were supplied to 4 different machine learning algorithms (MLAs) with an independent EDSS score prediction generated from each. These scores were compared with the clinically derived EDSS score to assess the similarity of the scores and to determine an accuracy estimate for each.
resultsOf 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 with 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.
conclusionsThe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.