The past 5–10 years have seen rapid advances in digital sensors and imaging-based technologies for the diagnosis of neurological conditions. However, the majority of these technologies are in the early stages of development — now is the time to consider how we validate these tools and safely integrate them into clinical practice.
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Reliability and acceptance of dreaMS, a software application for people with multiple sclerosis: a feasibility study
Journal of Neurology Open Access 30 August 2022
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Cohen, A. B. & Nahed, B. V. The digital neurologic examination. Digit. Biomark. 5, 114–126 (2021).
Woelfle, T. et al. Reliability and acceptance of a smartphone-based remote monitoring app (dreaMS) for people with MS–results of a feasibility study. Am. Acad. Neurol. https://index.mirasmart.com/aan2022/PDFfiles/AAN2022-002838.html (2022).
Montalban, X. et al. A smartphone sensor-based digital outcome assessment of multiple sclerosis. Mult. Scler. 28, 654–664 (2022).
Zhan, A. et al. Using smartphones and machine learning to quantify Parkinson disease severity: the mobile Parkinson disease score. JAMA Neurol. 75, 876–880 (2018).
Howett, D. et al. Differentiation of mild cognitive impairment using an entorhinal cortex-based test of virtual reality navigation. Brain 142, 1751–1766 (2019).
Pemberton, H. G. et al. Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review. Neuroradiology 63, 1773–1789 (2021).
Danelakis, A., Theoharis, T. & Verganelakis, D. A. Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging. Comput. Med. Imaging Graph. 70, 83–100 (2018).
Bivard, A., Churilov, L. & Parsons, M. Artificial intelligence for decision support in acute stroke — current roles and potential. Nat. Rev. Neurol. 16, 575–585 (2020).
Goldsack, J. C. et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for biometric monitoring technologies (BioMeTs). NPJ Digit. Med. 3, 55 (2020).
Walton, M. K. et al. Considerations for development of an evidence dossier to support the use of mobile sensor technology for clinical outcome assessments in clinical trials. Contemp. Clin. Trials 91, 105962 (2020).
We thank M. Bach Cuadra for the layout of Fig. 1.
The authors declare no competing interests.
Digital Medicine society (DiMe) Library of Digital Endpoints: https://www.dimesociety.org/communication-education/library-of-digital-endpoints/
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Granziera, C., Woelfle, T. & Kappos, L. Development and implementation of new diagnostic technologies in neurology. Nat Rev Neurol 18, 445–446 (2022). https://doi.org/10.1038/s41582-022-00692-z