Autonomous navigation of catheters and guidewires in mechanical thrombectomy using inverse reinforcement learning

Harry Robertshaw, Lennart Karstensen, Benjamin Jackson, Alejandro Granados, Thomas C. Booth

Abstract: Autonomous navigation of catheters and guidewires can enhance endovascular surgery safety and efficacy, reducing procedure times and operator radiation exposure. Integrating tele-operated robotics could widen access to time-sensitive emergency procedures like mechanical thrombectomy (MT). Reinforcement learning (RL) shows potential in endovascular navigation, yet its application encounters challenges without a reward signal. This study explores the viability of autonomous guidewire navigation in MT vasculature using inverse reinforcement learning (IRL) to leverage expert demonstrations.