  Purpose: 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 navigation in MT vasculature using inverse RL (IRL)
to leverage expert demonstrations. Methods: This study established a
simulation-based training and evaluation environment for MT navigation. We used
IRL to infer reward functions from expert behaviour when navigating a guidewire
and catheter. We utilized soft actor-critic to train models with various reward
functions and compared their performance in silico. Results: We demonstrated
feasibility of navigation using IRL. When evaluating single versus dual device
(i.e. guidewire versus catheter and guidewire) tracking, both methods achieved
high success rates of 95% and 96%, respectively. Dual-tracking, however,
utilized both devices mimicking an expert. A success rate of 100% and procedure
time of 22.6 s were obtained when training with a reward function obtained
through reward shaping. This outperformed a dense reward function (96%, 24.9 s)
and an IRL-derived reward function (48%, 59.2 s). Conclusions: We have
contributed to the advancement of autonomous endovascular intervention
navigation, particularly MT, by employing IRL. The results underscore the
potential of using reward shaping to train models, offering a promising avenue
for enhancing the accessibility and precision of MT. We envisage that future
research can extend our methodology to diverse anatomical structures to enhance
generalizability.
