Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks
- DOI
- 10.3389/fphys.2024.1288339
- Published
- 2024-02-21
- Container
- Frontiers in Physiology
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fphys.2024.1288339,
title = {Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks},
author = {Jakob Versnjak and Pavlo Yevtushenko and Titus Kuehne and Jan Bruening and Leonid Goubergrits},
year = {2024},
journal = {Frontiers in Physiology},
doi = {10.3389/fphys.2024.1288339},
url = {https://doi.org/10.3389/fphys.2024.1288339}
}RIS
TY - JOUR TI - Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks AU - Jakob Versnjak AU - Pavlo Yevtushenko AU - Titus Kuehne AU - Jan Bruening AU - Leonid Goubergrits PY - 2024 JO - Frontiers in Physiology DO - 10.3389/fphys.2024.1288339 UR - https://doi.org/10.3389/fphys.2024.1288339 ER -
APA
Versnjak, J., Yevtushenko, P., Kuehne, T., Bruening, J., & Goubergrits, L. (2024). Deep learning based assessment of hemodynamics in the coarctation of the aorta: comparison of bidirectional recurrent and convolutional neural networks. Frontiers in Physiology. https://doi.org/10.3389/fphys.2024.1288339
Source records
- crossref · retrieved 2026-09-25T17:13:55.446Z