Decision-based AI Visual Navigation for Cardiac Ultrasounds

• URL: http://arxiv.org/abs/2504.12535v1
• Date: Wed, 16 Apr 2025 23:54:46 GMT
• Title: Decision-based AI Visual Navigation for Cardiac Ultrasounds
• Authors: Andy Dimnaku, Dominic Yurk, Zhiyuan Gao, Arun Padmanabhan, Mandar Aras, Yaser Abu-Mostafa,
• Abstract summary: This paper demonstrates a novel AI navigation system that builds on a decision model for identifying the inferior vena cava (IVC) of the heart.<n>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.<n>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.
• Score: 0.7825791212345073
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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