Master-slave systems for endovascular catheterization have brought major clinical benefits including reduced radiation doses to the operators, improved precision and stability of the instruments, as well as reduced procedural duration. Emerging deep reinforcement learning (RL) technologies could potentially automate more complex endovascular tasks with enhanced success rates, more consistent motion and reduced fatigue and cognitive workload of the operators. However, the complexity of the pulsatile flows within the vasculature and non-linear behavior of the instruments hinder the use of model-based approaches for RL. This paper describes model-free generative adversarial imitation learning to automate a standard arterial catherization task. The automation policies have been trained in a pre-clinical setting. Detailed validation results show high success rates after skill transfer to a different vascular anatomical model. The quality of the catheter motions also shows less mean and maximum contact forces compared to manual-based approaches.

Emerging robot-assisted endovascular intervention has the potential to reduce X-ray radiations to the operator while enhancing the stability and dexterity of catheter manipulation. Supervised and shared autonomy of endovascular procedures could add further improvements in reduced fatigue and cognitive workloads of the operator, higher success rates of cannulation and improved surgical outcomes. However, robotic path planning for endovascular procedure is challenging due to complex and non-linear flow dynamics inside the vasculature. This paper presents a learning-based robotic catheterization platform addressing those challenges, this approach incorporates path integral reinforcement learning (RL) framework based on dynamic movement primitives (DMP) to enhance catheterization tasks by a customized robotic manipulator. The robotic trajectories were optimized through RL in order to avoid unwanted contacts between the catheter tip and the vessel wall. The proposed methods can adapt to different flow simulations, vascular models, and catheterization tasks. The quality of the catheterization was evaluated with performance metrics. The results show significant refinement of catheter paths by the proposed approach, resulting in shorter overall lengths and fewer contact forces, which can potentially reduce risks in endothelial wall damages, embolization, and stroke. The results support the development of robotic path planning for endovascular procedures as well as designing intelligent, hands-on robotic navigation platforms.

Increasing demands in endovascular intervention have motivated technical skill training and competency-based measures of performance. However, there are no well-established online metrics for technical skill assessment; few studies have explored operator behavioral patterns from catheter motion and operator hand motions. This paper proposes a platform for active online training and objective assessment of endovascular skills, through learning optimum catheter motions from multiple demonstrations. An ungrounded hand-held haptic device for providing intuitive haptic guidance to novice users based on this learnt information is also proposed. Statistical models are implemented to extract the underlying catheter motion patterns, and utilize them for performance evaluation and haptic guidance. The results show significant improvements in endovascular navigation for inexperienced operators. Finer catheter motions were achieved with the provided haptic guidance. The results suggest that the proposed platform can be integrated into current clinical training setups, and motivate the improvement of endovascular training platforms with better realism.

Despite the increased use of robotic catheter navigation systems, and the growing interest in surgical skill evaluation in the field of endovascular intervention, there is a lack of objective and quantitative metrics for performance evaluation. So far very little research has studied operator behavioral patterns using catheter kinematics, operator forces and motions, and catheter-tissue interactions. This paper proposes a framework for automated and objective assessment of performance by measuring catheter-tissue contact forces and operator motion patterns across different skill levels, and using language models to learn the underlying force and motion patterns that are characteristic of skill. Discrete HMMs are utilized to model operator behavior for varying skill levels performing different catheterization tasks, resulting in cross-validation classification accuracies of 94% (expert) and 98% (novice) using the force-based skill models, as well as 83% (expert) and 94% (novice) using the motion-based models. The results motivate the design of improved metrics for endovascular skill assessment with further applications towards performance evaluation of robot-assisted endovascular catheterization.