自主导线导航的零射击增强学习策略
Valentina Scarponi1,2, Michel Duprez1,2, Florent Nageotte2
1MIMESIS Team, Inria, Strasbourg, France.
概括
这项研究引入了一种新的零射击学习策略,用于心血管手术中自主导管导航. 该方法使人工智能能够在不需要重新培训的情况下对新的血管解剖学进行概括,提高效率并减少辐射暴露.
科学领域:
- 医疗机器人 医疗机器人
- 人工智能在医学中的应用
- 心血管干预 心血管干预
背景情况:
- 心血管疾病的治疗涉及复杂的导线和导管导航.
- 目前的方法导致了长时间的手术和患者/临床医生的X射线辐射暴露.
- 深度强化学习 (DRL) 显示了自动化导管导航的潜力,但在概括方面存在困难.
研究的目的:
- 开发自主内血管导航的零射击学习策略.
- 为了使DRL算法能够在没有重新训练的情况下对未见的血管解剖进行概括.
- 提高心血管疾病机器人干预的效率和安全性.
主要方法:
- 为3D自主内血管导航提出了一种零射击学习策略.
- 利用一个小型的训练集的分支模式进行强化学习.
- 开发了一种适用于新型血管几何学的控制算法.
主要成果:
- 在4个不同的血管系统上演示了该方法,在达到随机目标时的成功率为95%.
- 获得了计算效率高的培训,仅用2小时完成.
- 验证了算法的导航看不见的几何形状的能力.
结论:
- 拟议的训练方法可以导航未见的血管几何形状.
- 该策略利用近乎形状不变的观测空间进行概括.
- 这种方法为复杂干预中的自动化导管导航提供了一个有希望的解决方案.
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