Snapshot artificial intelligence-determination of ejection fraction from a single frame still image: a multi-institutional, retrospective model development and validation study.

Jeffrey G Malins¹, D M Anisuzzaman¹, John I Jackson¹, Eunjung Lee¹, Jwan A Naser¹, Behrouz Rostami¹, Jared G Bird¹, Dan Spiegelstein², Talia Amar², Jae K Oh¹, Patricia A Pellikka¹, Jeremy J Thaden¹, Francisco Lopez-Jimenez¹, Sorin V Pislaru¹, Paul A Friedman¹, Garvan C Kane¹, Zachi I Attia³

¹Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.

²UltraSight, Rehovot, Israel.

³Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA. Electronic address: attia.itzhak@mayo.edu.

PubMed DOI

Abstract

BACKGROUND: Artificial intelligence (AI) is poised to transform point-of-care practice by providing rapid snapshots of cardiac functioning. Although previous AI models have been developed to estimate left ventricular ejection fraction (LVEF), they have typically used video clips as input, which can be computationally intensive. In the current study, we aimed to develop an LVEF estimation model tha…

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