Transcatheter arterial chemoembolization (TACE) is the common choice of non-open surgery for hepatocellular carcinoma (HCC) now. In this study, a simple TACE robotic system of 4-degree-of-freedom is proposed to get higher accuracy and stability of the surgery operation and reduce X-ray exposure time of the surgeons. The master–slave control strategy is adopted in the robotic system and a customized sigmoid function is designed to optimize the joystick control of the master–slave robotic control system. A force-sensing module is developed to sense the resistance of the guide wire in linear delivery motion and an auxiliary bending feedback method based on constraint pipe with a film sensor is proposed. With two force-sensing methods, the safety strategy of robotic motion with 9 different motion constraint coefficients is given and a human–computer interface is developed. The TACE robot would monitor the value of the force sensor and the analog voltage of the film sensor to adopt the corresponding motion constraint coefficient in every 10 ms. Vascular model experiments were performed to validate the robotic system, and the results showed that the safety strategy could improve the reliability of the operation with immediate speed constraint and avoid potential aggressive delivery.

Vascular interventional surgery offers advantages, such as minimal invasiveness, quick recovery, and low side-effects. Performing automatic guidewire navigation on vascular surgical robots can effectively assist doctors in performing surgery. Deep learning and reinforcement learning methods have been widely used for guidewire navigation tasks. However, the challenge remains in making delivery decisions for complex and extended pathways, with real-time images being the only data source. The development of network architecture, coupled with the formulation of an efficacious training regimen for this network is of significant importance and holds substantial meaning for the advancement of autonomous systems in vascular surgical robots. Therefore, this research proposes a virtual training environment that incorporates real vascular projections to create virtual environment. In this environment, the approach is enhanced by incorporating guidewire tip-to-target distance in the reward function, using real-time images as input states. This article also employs a multiprocess proximal policy optimization algorithm to accelerate training process and a multistage training approach to reduce the training difficulty. Results demonstrate the effectiveness in virtual automated guidewire navigation and improves success rates. This research proposes a method, which generates effective inputs for the reinforcement learning agent, and enables the pretrained agent to accomplish delivery tasks in real-world scenarios.

Background: During transcatheter arterial chemoembolization (TACE), the delivery of a guidewire to the lesion site is a critical step, making the analysis and positioning of guidewire morphology crucial for both robotic systems and physicians in interventional surgeries. Current research on guidewires often faces challenges such as a low image signal-to-noise ratio and severe class imbalance. To overcome these issues and enhance the practical delivery of guidewires in clinical settings, this study introduces a comprehensive dataset for guidewire delivery during TACE and develops a specialized deep learning model for segmenting guidewire morphology in X-ray fluoroscopic images. Methods: We retrospectively collected 2,839 X-ray images acquired under real-time guidance from 38 subjects and manually annotated the guidewires. We proposed a deep learning-based guidewire segmentation method, which integrated two effective modules designed in this study: a bilateral feature fusion (BGA) module and a lightweight gated attention (SDA) module, achieving precise segmentation of guidewires in intraoperative images. Results: Quantitative and qualitative assessments were performed on 903 clinical images from 27 X-ray fluoroscopy videos. The segmentation network proposed in this paper demonstrated superior performance, achieving an area under the curve (AUC) of 91.64%, a Macro-F1 score of 85.63%, and a Dice coefficient of 71.29%. Conclusions: This study introduces a novel guidewire segmentation method specifically designed for clinical TACE. It not only assists physicians during interventional procedures but is also expected to be integrated into the intelligent systems of vascular interventional surgical robots, enabling robotic assistance in the future interventional surgeries.

Vascular interventional doctors are exposed to radiation hazards during surgery and endure high work intensity. Remote vascular interventional surgery robotics is a hot research field, in which researchers aim to not only protect the health of interventional doctors, but to also improve surgical accuracy and efficiency. However, the current vascular interventional robots have numerous shortcomings, such as poor haptic feedback, few compatible surgeries and instruments, and cumbersome maintenance and operational procedures. Nevertheless, vascular interventional surgery combined with robotics provides more cutting?edge directions, such as Internet remote surgery combined with 5G network technology and the application of artificial intelligence in surgical procedures. To summarize the developmental status and key technical points of intravascular interventional surgical robotics research, we performed a systematic literature search to retrieve original articles related to remote vascular interventional surgery robotics published up to December 2020. This review, which includes 113 articles published in English, introduces the mechanical and structural characteristics of various aspects of vascular interventional surgical robotics, discusses the current key features of vascular interventional surgical robotics in force sensing, haptic feedback, and control methods, and summarizes current frontiers in autonomous surgery, long-distance robotic telesurgery, and magnetic resonance imaging (MRI)-compatible structures. On the basis of summarizing the current research status of remote vascular interventional surgery robotics, we aim to propose a variety of prospects for future robotic systems

Force feedback is essential for safe endovascular teleoperation, yet typically constrained by complex sensor integration. This article presents a compact active haptic interface system designed for robotic catheterization. Leveraging the intrinsic proprioception of a Permanent Magnet Synchronous Motor, the integrated design enables proprioception proximal force measurement, simplifying mechanical structure while ensuring clinical deployability. To refine haptic fidelity, a hybrid estimation framework combines physics-based dynamics with a neural network to compensate for residual errors, optimizing the reconstruction of proximal interaction forces. The interface supports a continuous rate-control strategy, mapping estimated resistance into real-time active feedback. System experiments and user studies have demonstrated a delay of less than 5 ms and a estimation accuracy of 0.1 N. The proposed solution significantly reduces peak interaction forces and enhances obstacle awareness, offering a practical, cost-effective system-level solution for robotic vascular interventions.