Explicit and automatic ejection fraction assessment on 2D cardiac ultrasound with a deep learning-based approach.

Olivier Moal¹, Emilie Roger², Alix Lamouroux³, Chloe Younes⁴, Guillaume Bonnet⁵, Bertrand Moal⁶, Stephane Lafitte⁷

¹DESKi, Bordeaux, France. Electronic address: olivier.moal@deski.io.

²DESKi, Bordeaux, France. Electronic address: emilie.roger@deski.io.

³DESKi, Bordeaux, France. Electronic address: alix.lamouroux@deski.io.

⁴DESKi, Bordeaux, France. Electronic address: chloe.younes.v@gmail.com.

⁵Hôpital Cardiologique Haut Lévêque, CHU de Bordeaux, CIC 0005, Pessac, France. Electronic address: unbonnet@gmail.com.

⁶DESKi, Bordeaux, France. Electronic address: bertrand.moal@deski.io.

⁷Hôpital Cardiologique Haut Lévêque, CHU de Bordeaux, CIC 0005, Pessac, France. Electronic address: stephane.lafitte@chu-bordeaux.fr.

PubMed DOI

Abstract

BACKGROUND: Ejection fraction (EF) is a key parameter for assessing cardiovascular functions in cardiac ultrasound, but its manual assessment is time-consuming and subject to high inter and intra-observer variability. Deep learning-based methods have the potential to perform accurate fully automatic EF predictions but suffer from a lack of explainability and interpretability. This study proposes a…

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