Artificial intelligence detection of heart failure on coronary computed tomography angiography: external validation in patients with non-ST-segment elevation acute coronary syndrome.

Anne Sophie Overgaard Olesen^1,2,3, Kristina Cecilia Miger¹, Silas Nyboe Ørting⁴, Jens Petersen⁴, Marleen de Bruijne^4,5, Mikael Ploug Boesen^2,6, Klaus Fuglsang Kofoed^2,7,8, Johannes Grand⁹, Jens Jakob Thune^1,2, Alasdair D Henderson³, Pardeep S Jhund³, Lars Køber^2,9, Olav Wendelboe Nielsen¹

¹Department of Cardiology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Bispebjerg Bakke 23, 2400 Copenhagen, Denmark.

²Department of Clinical Medicine, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.

³British Heart Foundation Cardiovascular Research Centre, School of Cardiovascular and Metabolic Health, University of Glasgow, 126 University Place, G12 8TA Glasgow, UK.

⁴Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.

⁵Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, Netherlands.

⁶Department of Radiology, Copenhagen University Hospital-Bispebjerg and Frederiksberg, Copenhagen, Denmark.

⁷Department of Cardiology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.

⁸Department of Radiology, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.

⁹Department of Cardiology, Copenhagen University Hospital-Amager and Hvidovre, Hvidovre, Denmark.

PubMed PMC DOI PDF

Abstract

AIMS: Heart failure (HF) in non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is associated with poor prognosis but often under-recognized. Coronary computed tomography angiography (CCTA), increasingly used in NSTE-ACS, contains cardiopulmonary features not routinely assessed for HF. We evaluated whether an artificial intelligence (AI) algorithm applied to CCTA could identify HF likeliho…

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