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Robot-assisted systems for vascular interventional surgery (VIS) have the advantages of high precision and an improved operating environment for the surgeon. However, the current robot-assisted systems cannot completely replace human beings in controlling interventional devices—for example, rapid guidewire/catheter replacement and force feedback. In the face of these challenges, the robot-assisted system presented in this article can better solve the above problems. The experiments for the guidewire and catheter were designed and performed separately based on the developed robot-assisted system. The experimental results show that the participants can use the system to manipulate the guidewire and catheter to reach the designated blood vessel position. Based on the experiments for the catheter, for the first time, the reciprocating manipulation method with visual-based force feedback (VFF) was used for experimental evaluation. The experimental results show that this method can effectively avoid the buckling phenomenon of the catheter; the VFF plays a vital role in improving the safety of the operation and provides an operational assessment of VIS safety. In addition, this article puts forward the evaluation index for maximum pull force (MPLF) and force fluctuation, which provides an essential reference for enriching the evaluation of VIS technical skills.

Interventional surgery robots are essential in cardiovascular surgery as remote medical devices. By performing remote surgery, surgeons can reduce surgical fatigue and after-effects from heavy surgical instruments and radiation, ensuring that cardiovascular surgery is performed in a safe and reliable manner. To enhance stability during interventional procedures and reduce the impact of surgical risk due to factors where the robotic guidewire section from the end is vulnerable to mechanical jitter or blockage by blood flow, lipids, and thrombus inside the vessel, a new control method is proposed. The active disturbance rejection controller (ADRC) combined with intelligence algorithms is used to improve the performance of the controller by introducing the fuzzy inference algorithm and RBF neural network algorithm to self-adjust the parameters of the controller so that it has a greater ability to compensate for the disturbance factors appearing in the system. In numerical simulation experiments, the advantages and disadvantages of the ADRC combined with intelligence algorithms and the control performance of the conventional control strategy are analyzed in terms of the following: disturbance suppression performance and flexibility performance, respectively. Finally, different types of working conditions have been designed in the experimental platform to simulate the operation flow of in vivo vascular surgery. Experimental results show that the controller proposed in this paper meets the high accuracy, fast response, and low deviation required by interventional vascular surgery robots in complex surgical environments and can provide a more reliable guarantee for the stability of interventional surgery robots.

Cardiovascular diseases (CVDs) are the deadliest diseases worldwide. Master-slave robotic systems have been widely used in vascular interventional surgery with the benefit of high safety, efficient operation, and procedural facilitation. This paper introduces a remote-controlled vascular interventional robot (RVIR) that aims to enable surgeons to perform complex vascular interventions reliably and accurately under a magnetic resonance imaging (MRI) environment. The slave robot includes a guidewire manipulator (GM) and catheter manipulator (CM) that are mainly composed of a hollow driving mechanism and a linear motion platform. The hollow driving mechanism is based on a traveling wave-type hollow ultrasonic motor (HUM) which has high positional precision, fast response, and magnetic interference resistance and realizes the cooperation of the guidewire and catheter by omitting the redundant transmission mechanism and maintaining good coaxiality. The HUM stator, the core part of the RVIR, is optimized by an adaptive genetic algorithm for better quality and greater amplitude of traveling waves, which are beneficial to the drive efficiency and precision. The robot system features great cooperating performance, small hysteresis, and high kinematic accuracy and has been experimentally verified for its capability to precisely manipulate the guidewire and catheter.

In vascular interventional surgery, surgeons operate guidewires and catheters to diagnose and treat patients with the assistance of the digital subtraction angiography (DSA). Therefore, the surgeon will be exposed to X-rays for extended periods. To protect the surgeon, the development of a robot-assisted surgical system is of great significance. The displacement tracking accuracy is the most important issue to be considered in the development of the system. In this study, the active disturbance rejection control (ADRC) method is applied to guarantee displacement tracking accuracy. First, the core contents of the proportional–integral–derivative (PID) and ADRC methods are analyzed. Second, comparative evaluation experiments for incremental PID and ADRC methods are presented. The results show that the ADRC method has better performance of than that of the incremental PID method. Finally, the calibration experiments for the ADRC control method are implemented using the master–slave robotic system. These experiments demonstrate that the maximum tracking error is 0.87 mm using the ADRC method, effectively guaranteeing surgical safety.

A teleoperated robotic catheter operating system is a solution to avoid occupational hazards caused by repeated exposure radiation of the surgeon to X-ray during the endovascular procedures. However, inadequate force feedback and collision detection while teleoperating surgical tools elevate the risk of endovascular procedures. Moreover, surgeons cannot control the force of the catheter/guidewire within a proper range, and thus the risk of blood vessel damage will increase. In this paper, a magnetorheological fluid (MR)-based robot-assisted catheter/guidewire surgery system has been developed, which uses the surgeon’s natural manipulation skills acquired through experience and uses haptic cues to generate collision detection to ensure surgical safety. We present tests for the performance evaluation regarding the teleoperation, the force measurement, and the collision detection with haptic cues. Results show that the system can track the desired position of the surgical tool and detect the relevant force event at the catheter. In addition, this method can more readily enable surgeons to distinguish whether the proximal force exceeds or meets the safety threshold of blood vessels.

Robot-assisted interventional surgery can greatly reduce the radiation received by surgeons during the operation, but the lack of force detection and force feedback is still a risk in the operation which may harm the patient. In those robotic surgeries, the traditional force detection methods may have measurement losses and errors caused by mechanical transmission and cannot identify the direction of the force. In this paper, an interventional surgery robot system with a force detection device is designed and a new force detection method based on strain gauges is proposed to detect the force and infer the bending direction of the catheter in the vessel by using BP neural network. In addition, genetic algorithm is used to optimize the BP neural network, and the error between the calculated results and the actual results is reduced by 37%, which improves the accuracy of catheter bending recognition. Combining this new method with traditional force sensors not only reduces the error caused by the traditional mechanical transmission, but also can detect the bending direction of the catheter in the blood vessel, which greatly improves the safety of the operation.

Diabetic foot is a serious complication in patients with diabetes. It is caused by diabetes combined with lower extremity vascular lesions and neuropathy so that a wound does not heal for a long time. In severe cases, it may lead to bone marrow infection and bone destruction, with a high disability and fatality rate. This report describes the case of a patient with diabetic hallux gangrene with lower extremity vascular disease, which was treated by surgical debridement, ozone therapy, and vascular interventional surgery. Using this treatment, the diabetic foot can be cured better and faster, and a satisfactory effect can be achieved.

Background The emergence and development of robot assistant interventional vascular surgery technologies have benefited many patients with cardiovascular or cerebrovascular diseases. Due to the absence of effective training measures, these new advanced technologies have not been fully utilized and only few experienced surgeons can perform such complicated surgeries so far. In order to solve such problems, virtual reality based vascular interventional surgery training system, a promising way to train young surgeons or assist experienced surgeons to perform surgery, has been widely studied. Methods In this paper, we mainly conduct a thorough study on both reliable deformation and high real-time performance of an interactive surgery training system. An efficient hybrid geometric blood vessel model which handles the collision detection query and vascular deformation calculation separately is employed to enhance the real-time performance of our surgery training system. In addition, a position-based dynamic approach with volume conservation constraint is used to improve the vascular deformation result. Finally, a hash table based spatial adaptive acceleration algorithm which makes the training system much more efficient and reliable is described. Results Several necessary experiments are conducted to validate the vascular deformation scheme presented in this paper. From the results we can see that the position-based dynamic modeling method with volume conservation constraint can prevent the vascular deformation from the issue of penetration. In addition, the deformation calculation with spatial acceleration algorithm has enhanced the real-time performance significantly. Conclusion The corresponding experimental results indicate that both the hybrid geometric blood vessel model and the hash table based spatial adaptive acceleration algorithm can enhance the performance of our surgery training system greatly without losing the deformation accuracy.