Endovascular techniques have been embraced as a minimally-invasive treatment approach within different disciplines of interventional radiology and cardiology. The current practice of endovascular procedures, however, is limited by a number of factors including exposure to high doses of X-ray radiation, limited 3D imaging, and lack of contact force sensing from the endovascular tools and the vascular anatomy. More recently, advances in steerable catheters and development of master/slave robots have aimed to improve these practices by removing the operator from the radiation source and increasing the precision and stability of catheter motion with added degrees-of-freedom. Despite their increased application and a growing research interest in this area, many such systems have been designed without considering the natural manipulation skills and ergonomic preferences of the operators. Existing studies on tool interactions and natural manipulation skills of the operators are limited. In this manuscript, new technical developments in different aspects of robotic endovascular intervention including catheter instrumentation, intra-operative imaging and navigation techniques, as well as master/slave based robotic catheterization platforms are reviewed. We further address emerging trends and new research opportunities towards more widespread clinical acceptance of robotically assisted endovascular technologies.

Despite the increasing popularity of endovascular intervention in clinical practice, there remains a lack of objective and quantitative metrics for skill evaluation of endovascular techniques. Data relating to the forces exerted during endovascular procedures and the behavioral patterns of endovascular clinicians is currently limited. This research proposes two platforms for measuring tool forces applied by operators and contact forces resulting from catheter–tissue interactions, as a means of providing accurate, objective metrics of operator skill within a realistic simulation environment. Operator manipulation patterns are compared across different experience levels performing various complex catheterization tasks, and different performance metrics relating to tool forces, catheter motion dynamics, and forces exerted on the vasculature are extracted. The results depict significant differences between the two experience groups in their force and motion patterns across different phases of the procedures, with support vector machine (SVM) classification showing cross-validation accuracies as high as 90% between the two skill levels. This is the first robust study, validated across a large pool of endovascular specialists, to present objective measures of endovascular skill based on exerted forces. The study also provides significant insights into the design of optimized metrics for improved training and performance assessment of catheterization tasks.

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Despite rapid growth of robot assisted catheterization in recent years, most current platforms are based on master-slave designs with limited operator-robot collaborative control and automation. Under this setup, information concerning subject specific behavior and context-driven manoeuvre is not re-utilized for subsequent intervention. For endovascular catheterization, the robot itself is designed with little consideration of underlying skills and associated motion patterns. This paper proposes a learning-based approach for generating optimum motion trajectories from multiple demonstrations of a catheterization task such that it can be used for automating catheter motion within a collaborative setting. Motion models are generated from experienced manipulation of a catheterization procedure and replicated using a robotic catheter driver to assist inexperienced operators. Catheter tip motions of the automated approach are compared against the manual training sets for validating the proposed framework. The results show significant improvements in the quality of catheterization, which facilitate the design of hands-on collaborative robots that make full use of the natural skills of the operators.

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