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
https://github.com/mxochicale/rami-icra2023.

本文提出了一个用于机器人超声引导手术的技能转移学习的简单框架，该框架包括合适的取样技术、计算效率高的神经网络模型，实现了实时应用的技能转移学习。研究结果表明，经验丰富的临床医生比非临床医生在超声引导手术中具有更快和更顺畅的动作；为了实现实时应用，未来的工作需要使用剪枝和量化神经网络模型。