Echocardiogram Foundation Model -- Application 1: Estimating Ejection
  Fraction
Adil Dahlan Cyril Zakka Abhinav Kumar Laura Tang Rohan Shad Robyn Fong William Hiesinger
Cardiovascular diseases stand as the primary global cause of mortality. Among
the various imaging techniques available for visualising the heart and
evaluating its function, echocardiograms emerge as the preferred choice due to
their safety and low cost. Quantifying cardiac function based on
echocardiograms is very laborious, time-consuming and subject to high
interoperator variability. In this work, we introduce EchoAI, an echocardiogram
foundation model, that is trained using self-supervised learning (SSL) on 1.5
million echocardiograms. We evaluate our approach by fine-tuning EchoAI to
estimate the ejection fraction achieving a mean absolute percentage error of
9.40%. This level of accuracy aligns with the performance of expert
sonographers.