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

arXiv: https://arxiv.org/abs/1903.00586 · HTML: https://mlsanity.com/paper/1903.00586/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.00718/) (2021)
- [Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework](https://mlsanity.com/paper/2111.02167/) (2021)
- [Goal-conditioned reinforcement learning for ultrasound navigation guidance](https://mlsanity.com/paper/2405.01409/) (2024)
- [Learning Robotic Ultrasound Scanning Skills via Human Demonstrations and Guided Explorations](https://mlsanity.com/paper/2111.01625/) (2021)
- [Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft](https://mlsanity.com/paper/2106.05659/) (2021)
- [Follow Your Heart: Landmark-Guided Transducer Pose Scoring for Point-of-Care Echocardiography](https://mlsanity.com/paper/2603.27143/) (2026)

## Abstract (arXiv)

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.