Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation
- DOI
- 10.1016/j.jocmr.2024.101051
- Published
- 2024
- Container
- Journal of Cardiovascular Magnetic Resonance
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jocmr.2024.101051,
title = {Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation},
author = {Qiang Zhang and Anastasia Fotaki and Sona Ghadimi and Yu Wang and Mariya Doneva and Jens Wetzl and Jana G. Delfino and Declan P. O’Regan and Claudia Prieto and Frederick H. Epstein},
year = {2024},
journal = {Journal of Cardiovascular Magnetic Resonance},
doi = {10.1016/j.jocmr.2024.101051},
url = {https://doi.org/10.1016/j.jocmr.2024.101051}
}RIS
TY - JOUR TI - Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation AU - Qiang Zhang AU - Anastasia Fotaki AU - Sona Ghadimi AU - Yu Wang AU - Mariya Doneva AU - Jens Wetzl AU - Jana G. Delfino AU - Declan P. O’Regan AU - Claudia Prieto AU - Frederick H. Epstein PY - 2024 JO - Journal of Cardiovascular Magnetic Resonance DO - 10.1016/j.jocmr.2024.101051 UR - https://doi.org/10.1016/j.jocmr.2024.101051 ER -
APA
Zhang, Q., Fotaki, A., Ghadimi, S., Wang, Y., Doneva, M., Wetzl, J., Delfino, J. G., O’Regan, D. P., Prieto, C., & Epstein, F. H. (2024). Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence—review of evidence and proposition of a roadmap to clinical translation. Journal of Cardiovascular Magnetic Resonance. https://doi.org/10.1016/j.jocmr.2024.101051
Source records
- crossref · retrieved 2026-09-25T21:04:11.976Z