# Straight to the point: reinforcement learning for user guidance in ultrasound

## TL;DR

This paper proposes a reinforcement learning approach to guide inexperienced users in acquiring clinically relevant ultrasound images, specifically targeting cardiac views, to improve diagnostic data collection in emergency settings.

## Contribution

It introduces a novel RL-based method for user guidance in ultrasound imaging, focusing on novice users and specific cardiac views, which is a new application in this domain.

## Key findings

- Successfully guides users to correct sonic windows
- Enables acquisition of clinically relevant cardiac images
- Improves ultrasound image acquisition for inexperienced users

## Abstract

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.