Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

Development and implementation of new diagnostic technologies in neurology

Nature Reviews Neurology volume 18, pages 445–446 (2022)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Requirements for clinical translation of artificial intelligence-based algorithms.

References

  1. Cohen, A. B. & Nahed, B. V. The digital neurologic examination. Digit. Biomark. 5, 114–126 (2021).

    Article  Google Scholar 

  2. 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).

  3. Montalban, X. et al. A smartphone sensor-based digital outcome assessment of multiple sclerosis. Mult. Scler. 28, 654–664 (2022).

    Article  Google Scholar 

  4. 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).

    Article  Google Scholar 

  5. Howett, D. et al. Differentiation of mild cognitive impairment using an entorhinal cortex-based test of virtual reality navigation. Brain 142, 1751–1766 (2019).

    Article  Google Scholar 

  6. 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).

    Article  Google Scholar 

  7. 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).

    Article  Google Scholar 

  8. 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).

    Article  Google Scholar 

  9. 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).

    Article  Google Scholar 

  10. 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).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank M. Bach Cuadra for the layout of Fig. 1.

Author information

Authors and Affiliations

  1. Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland

    Cristina Granziera & Ludwig Kappos

  2. Department of Neurology, University Hospital Basel, Basel, Switzerland

    Cristina Granziera, Tim Woelfle & Ludwig Kappos

  3. Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland

    Cristina Granziera, Tim Woelfle & Ludwig Kappos

Authors
  1. Cristina Granziera
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tim Woelfle
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ludwig Kappos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cristina Granziera.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Related links

Digital Medicine society (DiMe) Library of Digital Endpoints: https://www.dimesociety.org/communication-education/library-of-digital-endpoints/

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41582-022-00692-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research