Harnessing Artificial Intelligence for Quantitative Assessment of Hemodynamic Congestion and Predicting Outcomes
(2021) In JACC: Cardiovascular Imaging 14(11). p.2120-2122
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/10c3e796-9417-4b51-b04b-88b4abc6d633
- author
- Ricci, Fabrizio LU and Khanji, Mohammed Y.
- organization
- publishing date
- 2021-11
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, congestion, pulmonary blood volume, pulmonary transit time, quantitative perfusion
- in
- JACC: Cardiovascular Imaging
- volume
- 14
- issue
- 11
- pages
- 3 pages
- publisher
- Elsevier
- external identifiers
-
- pmid:34147449
- scopus:85117582305
- ISSN
- 1936-878X
- DOI
- 10.1016/j.jcmg.2021.05.013
- language
- English
- LU publication?
- yes
- id
- 10c3e796-9417-4b51-b04b-88b4abc6d633
- date added to LUP
- 2021-11-19 14:29:34
- date last changed
- 2024-03-23 13:58:45
@misc{10c3e796-9417-4b51-b04b-88b4abc6d633,
author = {{Ricci, Fabrizio and Khanji, Mohammed Y.}},
issn = {{1936-878X}},
keywords = {{artificial intelligence; congestion; pulmonary blood volume; pulmonary transit time; quantitative perfusion}},
language = {{eng}},
number = {{11}},
pages = {{2120--2122}},
publisher = {{Elsevier}},
series = {{JACC: Cardiovascular Imaging}},
title = {{Harnessing Artificial Intelligence for Quantitative Assessment of Hemodynamic Congestion and Predicting Outcomes}},
url = {{http://dx.doi.org/10.1016/j.jcmg.2021.05.013}},
doi = {{10.1016/j.jcmg.2021.05.013}},
volume = {{14}},
year = {{2021}},
}