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.

  • News & Views
  • Published:

MACHINE LEARNING

The next step in deep learning-guided clinical trials

A combined imaging–clinical risk prediction model with the use of deep learning seems a promising approach for predicting sudden cardiac death in patients with ischemic cardiomyopathies. Deep-learning-guided clinical trials will be needed to translate this model into clinical practice.

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

Access options

Buy this article

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

Fig. 1: An example of potential deep-learning-guided intervention trials.

References

  1. Di Marco, A. et al. JACC Heart Fail. 5, 28–38 (2017).

    Article  PubMed  Google Scholar 

  2. Ommen, S. R. et al. Circulation 142, e533–e557 (2020).

    PubMed  Google Scholar 

  3. Al-Khatib, S. M. et al. Heart Rhythm. 15, e190–e252 (2018).

    Article  PubMed  Google Scholar 

  4. Krittanawong, C. et al. Eur. Heart J. 40, 2058–2073 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Popescu et al. Nat. Cardiovasc. Res. https://doi.org/10.1038/s44161-022-00041-9 (2022).

    Article  Google Scholar 

  6. Soroush, N. et al. Eur. Heart J. 41, ehaa946 (2020). 3394.

    Article  Google Scholar 

  7. Pepe, M. S., Fan, J. & Seymour, C. W. Acad. Radiol. 20, 863–873 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Yancy, C. W. et al. Circulation 134, e282–93 (2016).

    PubMed  Google Scholar 

  9. Maron, D. J. et al. N. Engl. J. Med. 382, 1395–1407 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Rodrigues, P. et al. Circ. Cardiovasc. Imaging 10, e006709 (2017).

    Article  PubMed  Google Scholar 

  11. Maliamanis, T. P. et al. in Machine Learning, Big Data, and IoT for Medical Informatics 1st edn (eds. Kumar, P. et al.) 53–70 (Elsevier, 2021).

  12. Ferreira, P. F. et al. J. Cardiovasc. Magn. Reson. 15, 41 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nieradzik, L. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2109.00903 (2022).

  14. Keras. https://keras.io/api/optimizers/ (accessed 17 February 2022).

  15. Bozkurt, B. et al. J. Card. Fail. S1071–S9164 (2021).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chayakrit Krittanawong.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Krittanawong, C. The next step in deep learning-guided clinical trials. Nat Cardiovasc Res 1, 286–288 (2022). https://doi.org/10.1038/s44161-022-00044-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44161-022-00044-6

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing