Summary of A Zero-shot Reinforcement Learning Strategy For Autonomous Guidewire Navigation, by Valentina Scarponi (mimesis et al.
A Zero-Shot Reinforcement Learning Strategy for Autonomous Guidewire Navigation
by Valentina Scarponi, Michel Duprez, Florent Nageotte, Stéphane Cotin
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Medical Physics (physics.med-ph)
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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 proposed paper presents a zero-shot learning strategy for three-dimensional autonomous endovascular navigation using Deep Reinforcement Learning approaches. The goal is to automate catheter navigation during robotized interventions in cardiovascular diseases treatment, reducing exposure to X-ray radiation and shortening procedure duration. The method uses a small training set of branching patterns, allowing the algorithm to generalize to unseen vascular anatomies without retraining. The results demonstrate an average success rate of 95% at reaching random targets on different vascular systems, with a computationally efficient training process taking only 2 hours. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper aims to make catheter navigation in cardiovascular disease treatment more efficient and automated. A new way of learning is developed that doesn’t need much training data. The algorithm can be applied to different blood vessel shapes without needing to learn all over again. This could lead to faster and safer procedures for patients. |
Keywords
* Artificial intelligence * Reinforcement learning * Zero shot
