Automatic control of cardiac ablation catheter with deep reinforcement learning method

To reduce the radiation exposure of personnel during an interventional procedure for arrhythmia, a robot has been developed and implemented herein for use in interventional procedures. Studies on the control of an electrophysiology catheter by robots are being conducted. However, controlling a catheter using a robot has limited precision owing to external forces subjected on the catheter due to blood flow and pulse inside a heart. This study implements a reinforcement learning method for automated control of a catheter by a robot. Using the reinforcement learning method, this study aims to show that such a robot can learn to manipulate a catheter to reach a target in a simulated environment and subsequently control a catheter in an actual environment. Randomization noise is used during the simulation to reduce the differences between the simulation and actual learning environments. Each environment is implemented with different movement values depending on insertion angles and steps of the catheter model. When the results from the simulated learning model are implemented in the actual environment, the success rate of catheter reaching the designated target is 73 %. In addition, the noise-implemented model shows that the success rate can be increased up to 87 %. Through these experiments, the study verifies that a simulated learning model can be implemented in a robot system to control an actual catheter as well as that the success rate of the model can be increased using randomization noise.