Decision-based AI Visual Navigation for Cardiac Ultrasounds
Journal:
arXiv
Published Date:
Apr 16, 2025
Abstract
Ultrasound imaging of the heart (echocardiography) is widely used to diagnose
cardiac diseases. However, obtaining an echocardiogram requires an expert
sonographer and a high-quality ultrasound imaging device, which are generally
only available in hospitals. Recently, AI-based navigation models and
algorithms have been used to aid novice sonographers in acquiring the
standardized cardiac views necessary to visualize potential disease
pathologies. These navigation systems typically rely on directional guidance to
predict the necessary rotation of the ultrasound probe. This paper demonstrates
a novel AI navigation system that builds on a decision model for identifying
the inferior vena cava (IVC) of the heart. The decision model is trained
offline using cardiac ultrasound videos and employs binary classification to
determine whether the IVC is present in a given ultrasound video. The
underlying model integrates a novel localization algorithm that leverages the
learned feature representations to annotate the spatial location of the IVC in
real-time. Our model demonstrates strong localization performance on
traditional high-quality hospital ultrasound videos, as well as impressive
zero-shot performance on lower-quality ultrasound videos from a more affordable
Butterfly iQ handheld ultrasound machine. This capability facilitates the
expansion of ultrasound diagnostics beyond hospital settings. Currently, the
guidance system is undergoing clinical trials and is available on the Butterfly
iQ app.
