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.

Accurate real-time catheter segmentation is an important pre-requisite for robot-assisted endovascular intervention. Most of the existing learning-based methods for catheter segmentation and tracking are only trained on smallscale datasets or synthetic data due to the difficulties of ground-truth annotation. Furthermore, the temporal continuity in intraoperative imaging sequences is not fully utilised. In this paper, we present FW-Net, an end-to-end and real-time deep learning framework for endovascular intervention. The proposed FW-Net has three modules: a segmentation network with encoder-decoder architecture, a flow network to extract optical flow information, and a novel flow-guided warping function to learn the frame-to-frame temporal continuity. We show that by effectively learning temporal continuity, the network can successfully segment and track the catheters in real-time sequences using only raw ground-truth for training. Detailed validation results confirm that our FW-Net outperforms stateof-the-art techniques while achieving real-time performance.

Cardiovascular diseases remain as the most common cause of death worldwide. Remotely manipulated robotic systems are utilized to perform minimally invasive endovascular interventions. The main benefits of this methodology include reduced recovery time, improvement of clinical skills and procedural facilitation. Currently, robotic assistance, precision, and stability of instrument manipulation are compensated by the lack of haptic feedback and an excessive amount of radiation to the patient. This paper proposes a novel master-slave robotic platform that aims to bring the haptic feedback benefit on the master side, providing an intuitive user interface, and clinical familiar workflow. The slave robot is capable of manipulating conventional catheters and guidewires in multi-modal imaging environments. The system has been initially tested in a phantom cannulation study under fluoroscopic guidance, evaluating its reliability and procedural protocol. As the slave robot has been entirely produced by additive manufacturing and using pneumatic actuation, MR compatibility is enabled and was evaluated in a preliminary study. Results of both studies strongly support the applicability of the robot in different imaging environments and prospective clinical translation.

Vascular diseases are the most common precursors to ischemic heart disease and stroke, which are two of the leading causes of death worldwide. Endovascular intervention is a minimally invasive surgical approach to treat such diseases. Compared to open surgery, it has the advantages of faster recovery, reduced need for general anesthesia, reduced blood loss and significantly lower mortality. Endovascular procedures require high surgical skills to minimize contacts between the manipulated instruments (catheters and guidewires) and the vessel wall, which represent one of the major risks for the patient. Robotic assistance can potentially improve the precision and stability of instruments manipulation. One key limitation of current commercial robotic platforms is the lack of haptic feedback, preventing their acceptance and limiting the clinical usability. This paper proposes to bring the benefit of haptic feedback to robot-assisted endovascular intervention. Here we hypothesize that the introduction of 3D haptic guidance during robot-assisted endovascular procedure can further improve the surgical performance and safety while overcoming the limitations of currently available technology. The proposed 3D haptic guidance allows the surgeon to sense the vasculature while controlling a catheter through a robotic haptic manipulator. Validation of the system is performed through end-user experiments with vascular surgeons on a bespoke surgical simulator. The obtained results demonstrate that 3D haptic guidance has the potential of improving effectiveness, precision, and safety of endovascular intervention. Furthermore, vascular surgeons found the proposed technology safe and overall easy to use, indicating its potential on real surgical procedures.

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.

Robotic and computer assistance can bring significant benefits to endovascular procedures in terms of precision and stability, reduced radiation doses, improved comfort and access to difficult and tortuous anatomy. However, the design of current commercially available platforms tends to alter the natural bedside manipulation skills of the operator, so that the manually acquired experience and dexterity are not well utilized. Furthermore, most of these systems lack of haptic feedback, preventing their acceptance and limiting the clinical usability. In this paper a new robotic platform for endovascular catheterization, the CathBot, is presented. It is an ergonomic master-slave system with navigation system and integrated vision-based haptic feedback, designed to maintain the natural bedside skills of the vascular surgeon. Unlike previous work reported in literature, dynamic motion tracking of both the vessel walls the catheter tip is incorporated to create dynamic active constraints. The system was evaluated through a combined quantitative and qualitative user study simulating catheterization tasks on a phantom. Forces exerted on the phantom were measured. The results showed a 70% decrease in mean force and 61% decrease in maximum force when force feedback is provided. This research provides the first integration of vision-based dynamic active constraints within an ergonomic robotic catheter manipulator. The technological advances presented here, demonstrates that vision-based haptic feedback can improve the effectiveness, precision, and safety of robot-assisted endovascular procedures.

The integration of minimally invasive robotic assistance and image-guidance can have positive impact on joint fracture surgery, providing a better clinical outcome with respect to the current open procedure. In this paper, a new design of the RAFS surgical system is presented. The redesign of the robotic system and its integration with a novel 3D navigation system through a new clinical workflow, overcomes the drawbacks of the earlier prototype. This makes the RAFS surgical system more suitable to clinical scenarios in the operating theatre. System accuracy and effectiveness are successfully demonstrated through laboratory trials and preliminary cadaveric trials. The experimental results demonstrate that the system allows the surgeon to reduce a 2-fragment distal femur fracture in a cadaveric specimen, with a reduction accuracy of up to 0.85 mm and 2.2°. Preliminary cadaveric trials also provided a positive and favorable outcome pointing to the usability and safety of the RAFS system in the operating theatre, potentially enhancing the capacity of joint fracture surgeries.

Robotic assistance can bring significant improvements to orthopedic fracture surgery: facilitate more accurate fracture fragment repositioning without open access and obviate problems related to the current minimally invasive fracture surgery techniques by providing a better clinical outcome, reduced recovery time, and health-related costs. This paper presents a new design of the robot-assisted fracture surgery (RAFS) system developed at Bristol Robotics Laboratory, featuring a new robotic architecture, and real-time 3D imaging of the fractured anatomy. The technology presented in this paper focuses on distal femur fractures, but can be adapted to the larger domain of fracture surgeries, improving the state-of-the-art in robot assistance in orthopedics. To demonstrate the enhanced performance of the RAFS system, 10 reductions of a distal femur fracture are performed using the system on a bone model. The experimental results clearly demonstrate the accuracy, effectiveness, and safety of the new RAFS system. The system allows the surgeon to precisely reduce the fractures with a reduction accuracy of 1.15 mm and 1.3°, meeting the clinical requirements for this procedure.

State-of-the-art laser phonomicrosurgery (LP) used for the treatment of laryngeal abnormalities involves complex otolaryngological surgical techniques. It relies heavily on surgeon dexterity, requiring significant psychomotor skills. Equipment scale and size, laser operative distance, and the anatomically small nature of the vocal folds all combine to compound the surgical challenges. An objective measurement is therefore necessary to understand the impact of equipment design, its usability, surgeon skill, and learning, on performing LP effectively. This paper introduces imaging based feature extraction as a method to establish metrics to assess surgical performance in LP. Experimental analysis demonstrates the utility of these metrics in measuring surgical task execution vis-à-vis the task objectives. The metrics also provide for a combined rating scale giving a robust quantitative classification of the levels of surgical performance.

A medical robotic system for teleoperated laser microsurgery based on a concept we have called “virtual scalpel” is presented in this paper. This system allows surgeries to be safely and precisely performed using a graphics pen directly over a live video from the surgical site. This is shown to eliminate hand-eye coordination problems that affect other microsurgery systems and to make full use of the operator's manual dexterity without requiring extra training. The implementation of this system, which is based on a tablet PC and a new motorized laser micromanipulator offering 1μm aiming accuracy within the traditional line-of-sight 2D operative space, is fully described. This includes details on the system's hardware and software structures and on its calibration process, which is essential for guaranteeing precise matching between a point touched on the live video and the laser aiming point at the surgical site. Together, the new hardware and software structures make both the calibration parameters and the laser aiming accuracy (on any plane orthogonal to the imaging axis) independent of the target distance and of its motions. Automatic laser control based on new intraoperative planning software and safety improvements based on virtual features are also described in this paper, which concludes by presenting results from sets of path following evaluation experiments conducted with 10 different subjects. These demonstrate an error reduction of almost 50% when using the virtual scalpel system versus the traditional laser microsurgery setup, and an 80% error reduction when using the automatic laser control routines, evidencing great improvements in terms of precision and controllability, and suggesting that the technological advances presented herein will lead to a significantly enhanced capacity for treating a variety of internal human pathologies.