Path planning for endovascular catheterization under curvature constraints via two-phase searching approach

A review on tissue-needle interaction and path planning models for bevel tip type flexible needle minimal intervention

A flexible needle has emerged as a crucial clinical technique in contemporary medical practices, particularly for minimally invasive interventions. Its applicability spans diverse surgical domains such as brachytherapy, cardiovascular surgery, neurosurgery and others. Notably, flexible needles find utility in biopsies requiring deep skin penetration to access infected areas. Despite its minimally invasive advantages, the precise guidance of the needle to its intended target, while avoiding damage to bones, blood vessels, organs and tissues, remains a significant challenge for researchers. Consequently, extensive research has been dedicated to enhancing the steering and accuracy of flexible needles. Here, we aim to elucidate the recent advancements, trends and perspectives in flexible needle steering models and path planning over the last 15 years. The discussed models encompass various types, including symmetric-tip needles, curved-tip needles, tendon-actuated needles, programmable needles and the innovative fracture-directed waterjet needles. Moreover, the paper offers a comprehensive analysis, comparing the trajectories followed by these needle models to attain the desired target with minimal tissue damage. By delving into these aspects, the paper contributes to a deeper understanding of the current landscape of flexible needle technology and guides future research directions in this dynamic field.

Enhancing percutaneous coronary intervention with heuristic path planning and deep-learning-based vascular segmentation

Percutaneous coronary intervention (PCI) is a minimally invasive technique for treating vascular diseases. PCI requires precise and real-time visualization and guidance to ensure surgical safety and efficiency. Existing mainstream guiding methods rely on hemodynamic parameters. However, these methods are less intuitive than images and pose some challenges to the decision-making of cardiologists. This paper proposes a novel PCI guiding assistance system by combining a novel vascular segmentation network and a heuristic intervention path planning algorithm, providing cardiologists with clear and visualized information. A dataset of 1077 DSA images from 288 patients is also collected in clinical practice. A Likert Scale is also designed to evaluate system performance in user experiments. Results of user experiments demonstrate that the system can generate satisfactory and reasonable paths for PCI. Our proposed method outperformed the state-of-the-art baselines based on three metrics (Jaccard: 0.4091, F1: 0.5626, Accuracy: 0.9583). The proposed system can effectively assist cardiologists in PCI by providing a clear segmentation of vascular structures and optimal real-time intervention paths, thus demonstrating great potential for robotic PCI autonomy.

Position-based dynamics simulator of vessel deformations for path planning in robotic endovascular catheterization

A major challenge during autonomous navigation in endovascular interventions is the complexity of operating in a deformable but constrained workspace with an instrument. Simulation of deformations for it can provide a cost-effective training platform for path planning. Aim of this study is to develop a realistic, auto-adaptive, and visually plausible simulator to predict vessels' global deformation induced by the robotic catheter's contact and cyclic heartbeat motion. Based on a Position-based Dynamics (PBD) approach for vessel modeling, Particle Swarm Optimization (PSO) algorithm is employed for an auto-adaptive calibration of PBD deformation parameters and of the vessels movement due to a heartbeat. In-vitro experiments were conducted and compared with in-silico results. The end-user evaluation results were reported through quantitative performance metrics and a 5-Point Likert Scale questionnaire. Compared with literature, this simulator has an error of 0.23±0.13% for deformation and 0.30±0.85mm for the aortic root displacement. In-vitro experiments show an error of 1.35±1.38mm for deformation prediction. The end-user evaluation results show that novices are more accustomed to using joystick controllers, and cardiologists are more satisfied with the visual authenticity. The real-time and accurate performance of the simulator make this framework suitable for creating a dynamic environment for autonomous navigation of robotic catheters.

Autonomous Obstacle Avoidance Path Planning for Grasping Manipulator Based on Elite Smoothing Ant Colony Algorithm

Assembly robots have become the core equipment of high-precision flexible automatic assembly systems with a small working range. Among different fields of robot technology, path planning is one of the most important branches. In the present study, an elite smoothing ant colony algorithm (ESACO) is proposed for spatial obstacle avoidance path planning of the grasping manipulator. In this regard, the state transition probability and pheromone update strategies are improved to enhance the search capability of path planning symmetry and the convergence of the algorithm. Then a segmented B-spline curve is presented to eliminate path folding points and generate a smooth path. Finally, a manipulator control system based on the Arduino Uno microcontroller is designed to drive the manipulator according to the planned trajectory. The experimental results show that the performance of the ESACO algorithm in different scenarios has symmetry advantages, and the manipulator can efficiently complete the simulation trajectory with high accuracy. The proposed algorithm provides a feasible scheme for the efficient planning of manipulators in equipment manufacturing workshops.

Physics-Informed Modeling and Control of Multi-Actuator Soft Catheter Robots

Catheter-based endovascular interventional procedures have become increasingly popular in recent years as they are less invasive and patients spend less time in the hospital with less recovery time and less pain. These advantages have led to a significant growth in the number of procedures that are performed annually. However, it is still challenging to position a catheter in a target vessel branch within the highly complicated and delicate vascular structure. In fact, vessel tortuosity and angulation, which cause difficulties in catheterization and reaching the target site, have been reported as the main causes of failure in endovascular procedures. Maneuverability of a catheter for intravascular navigation is a key to reaching the target area; ability of a catheter to move within the target vessel during trajectory tracking thus affects to a great extent the length and success of the procedure. To address this issue, this paper models soft catheter robots with multiple actuators and provides a time-dependent model for characterizing the dynamics of multi-actuator soft catheter robots. Built on this model, an efficient and scalable optimization-based framework is developed for guiding the catheter to pass through arteries and reach the target where an aneurysm is located. The proposed framework models the deflection of the multi-actuator soft catheter robot and develops a control strategy for movement of catheter along a desired trajectory. This provides a simulation-based framework for selection of catheters prior to endovascular catheterization procedures, assuring that given a fixed design, the catheter is able to reach the target location. The results demonstrate the benefits that can be achieved by design and control of catheters with multiple number of actuators for navigation into small vessels.

Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.