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Harnessing Artificial Intelligence for Quantitative Assessment of Hemodynamic Congestion and Predicting Outcomes

Fabrizio Ricci1, Mohammed Y Khanji2

  • 1Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy; Department of Clinical Sciences, Lund University, Malmö, Sweden; Casa di Cura Villa Serena, Città Sant'Angelo, Pescara, Italy.

JACC. Cardiovascular Imaging
|June 20, 2021
PubMed
Abstract

No abstract available in PubMed .

Keywords:
artificial intelligencecongestionpulmonary blood volumepulmonary transit timequantitative perfusion

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