# Towards a Simple Framework of Skill Transfer Learning for Robotic Ultrasound-guidance Procedures

arXiv: https://arxiv.org/abs/2305.04004 · HTML: https://mlsanity.com/paper/2305.04004/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 6 May 2023.

## Builds on (most cited first, 2 of 2)

- [Automatic Probe Movement Guidance for Freehand Obstetric Ultrasound](https://mlsanity.com/paper/2007.04480/) (2020)
- [Learning Ultrasound Scanning Skills from Human Demonstrations](https://mlsanity.com/paper/2111.09739/) (2021)

## Abstract (arXiv)

In this paper, we present a simple framework of skill transfer learning for robotic ultrasound-guidance procedures. We briefly review challenges in skill transfer learning for robotic ultrasound-guidance procedures. We then identify the need of appropriate sampling techniques, computationally efficient neural networks models that lead to the proposal of a simple framework of skill transfer learning for real-time applications in robotic ultrasound-guidance procedures. We present pilot experiments from two participants (one experienced clinician and one non-clinician) looking for an optimal scanning plane of the four-chamber cardiac view from a fetal phantom. We analysed ultrasound image frames, time series of texture image features and quaternions and found that the experienced clinician performed the procedure in a quicker and smoother way compared to lengthy and non-constant movements from non-clinicians. For future work, we pointed out the need of pruned and quantised neural network models for real-time applications in robotic ultrasound-guidance procedure. The resources to reproduce this work are available at urlhttps://github.com/mxochicale/rami-icra2023.