Summary of Autonomous Navigation Of Catheters and Guidewires in Mechanical Thrombectomy Using Inverse Reinforcement Learning, by Harry Robertshaw et al.
Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning
by Harry Robertshaw, Lennart Karstensen, Benjamin Jackson, Alejandro Granados, Thomas C. Booth
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the viability of autonomous navigation in mechanical thrombectomy (MT) vasculature using inverse reinforcement learning (IRL). The authors aim to develop a system that can efficiently and accurately navigate guidewires and catheters during MT procedures, reducing procedure times and operator radiation exposure. They use IRL to infer reward functions from expert demonstrations and train models with various reward functions. The results show that the system can achieve high success rates in simulating MT navigation, with the best performance achieved when using a reward function obtained through reward shaping. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous navigation of catheters and guidewires could make endovascular surgery safer and more effective. Researchers want to use robots to help with this process, but it’s tricky because there’s no clear way to tell if they’re doing a good job. This study looks at how well machines can learn to navigate blood vessels on their own using a type of learning called inverse reinforcement learning (IRL). They test different ways of teaching the machines and find that one method works much better than others. |
Keywords
» Artificial intelligence » Reinforcement learning
