Straight to the point: reinforcement learning for user guidance in ultrasound
This addresses the problem of enabling inexperienced healthcare professionals to perform point-of-care ultrasound effectively, though it is incremental as it builds on existing RL methods for a specific medical application.
The paper tackles the challenge of guiding novice users to acquire diagnostic-quality ultrasound images by developing a reinforcement learning strategy that directs them to the correct sonic window for cardiac imaging, specifically in the parasternal long axis view of the left ventricle.
Point of care ultrasound (POCUS) consists in the use of ultrasound imaging in critical or emergency situations to support clinical decisions by healthcare professionals and first responders. In this setting it is essential to be able to provide means to obtain diagnostic data to potentially inexperienced users who did not receive an extensive medical training. Interpretation and acquisition of ultrasound images is not trivial. First, the user needs to find a suitable sound window which can be used to get a clear image, and then he needs to correctly interpret it to perform a diagnosis. Although many recent approaches focus on developing smart ultrasound devices that add interpretation capabilities to existing systems, our goal in this paper is to present a reinforcement learning (RL) strategy which is capable to guide novice users to the correct sonic window and enable them to obtain clinically relevant pictures of the anatomy of interest. We apply our approach to cardiac images acquired from the parasternal long axis (PLAx) view of the left ventricle of the heart.