A Zero-Shot Reinforcement Learning Strategy for Autonomous Guidewire Navigation

arXiv:2403.02777v1 Announce Type: new
Abstract: Purpose: The treatment of cardiovascular diseases requires complex and challenging navigation of a guidewire and catheter. This often leads to lengthy interventions during which the patient and clinician are exposed to X-ray radiation. Deep Reinforcement Learning approaches have shown promise in learning this task and may be the key to automating catheter navigation during robotized interventions. Yet, existing training methods show limited capabilities at generalizing to unseen vascular anatomies, requiring to be retrained each time …

abstract arxiv autonomous clinician cs.lg cs.ro diseases leads navigation patient physics.med-ph ray reinforcement reinforcement learning strategy treatment type x-ray zero-shot