Deep reinforcement learning for guidewire navigation in coronary artery phantom
This work addresses the challenge of precise guidewire manipulation in coronary artery procedures, which is incremental by applying existing RL methods to a new medical robotics domain.
The paper tackled autonomous guidewire navigation in robot-assisted coronary interventions by developing a deep reinforcement learning framework, resulting in the guidewire reaching all valid targets in a stable phase.
In percutaneous intervention for treatment of coronary plaques, guidewire navigation is a primary procedure for stent delivery. Steering a flexible guidewire within coronary arteries requires considerable training, and the non-linearity between the control operation and the movement of the guidewire makes precise manipulation difficult. Here, we introduce a deep reinforcement learning(RL) framework for autonomous guidewire navigation in a robot-assisted coronary intervention. Using Rainbow, a segment-wise learning approach is applied to determine how best to accelerate training using human demonstrations with deep Q-learning from demonstrations (DQfD), transfer learning, and weight initialization. `State' for RL is customized as a focus window near the guidewire tip, and subgoals are placed to mitigate a sparse reward problem. The RL agent improves performance, eventually enabling the guidewire to reach all valid targets in `stable' phase. Our framework opens anew direction in the automation of robot-assisted intervention, providing guidance on RL in physical spaces involving mechanical fatigue.