Toward Task Autonomy in Robotic Cardiac Ablation: Learning-Based Kinematic Control of Soft Tendon-Driven Catheters

YoMo: Yoshimura Continuum Manipulator for MR Environment

Origami robots have garnered attention due to their versatile deformation and potential applications, particularly for medical applications. In this article, we propose a Yoshimura continuum manipulator (YoMo) that can achieve accurate control of the tip position for the magnetic resonance (MR) environment. The YoMo made of a single piece of paper is cable-actuated to generate the bending and shortening deformation. The paper-based YoMo attached to an arc frame can readily function under different orientations in the MR environment. The design and fabrication of YoMo were formulated according to the Yoshimura folding pattern. The kinematics model based on constant curvature assumption was derived as a benchmark method to predict the tip position of the YoMo. The Koopman operator theory was applied to describe the relationship between the tip position and the length change under different orientations. The linear quadratic regulator integrated into the Koopman-based model (K-LQR) was adopted to achieve the trajectory tracking. Comprehensive experiments were carried out to examine the proposed YoMo, its modeling and control methods. The performance of the YoMo including stiffness and workspace was characterized via a customized test setup. The Koopman-based method demonstrates the superiority over the constant curvature-based model to predict the tip position. The K-LQR control method was examined with different trajectories, and the impact of the orientation, speed, and different trajectories were taken into consideration. The results demonstrate the YoMo is capable of achieving trajectory tracking with satisfied accuracy, indicating its potential for medical applications in the MR environment.

Advanced Robotics for the Next-Generation of Cardiac Interventions

With an increasing number of elderly individuals, the demand for advanced technologies to treat cardiac diseases has become more critical than ever. Additionally, there is a pressing need to reduce the learning curve for cardiac interventionalists to keep pace with the rapid development of new types of procedures and devices and to expand the adoption of established procedures in more hospitals. This comprehensive review aims to shed light on recent advancements in novel robotic systems for cardiac interventions. To do so, this review provides a brief overview of the history of previously developed robotic systems and describes the necessity for advanced technologies for cardiac interventions to address the technological limitations of current systems. Moreover, this review explores the potential of cutting-edge technologies and methods in developing the next generation of intra-procedure autonomous navigation. Each highlighted topic undergoes a critical analysis to evaluate its technical limitations and the challenges that must be addressed for successful clinical implementation.

A universal calibration framework for mixed-reality assisted surgery

BACKGROUND

Mixed-reality-assisted surgery has become increasingly prominent, offering real-time 3D visualization of target anatomy such as tumors. These systems facilitate translating preoperative 3D surgical plans to the patient's body intraoperatively and allow for interactive modifications based on the patient's real-time conditions. However, achieving sub-millimetre accuracy in mixed-reality (MR) visualization and interaction is crucial to mitigate device-related risks and enhance surgical precision.

OBJECTIVE

Given the critical role of camera calibration in hologram-to-patient anatomy registration, this study aims to develop a new device-agnostic and robust calibration method capable of achieving sub-millimetre accuracy, addressing the prevalent uncertainties associated with MR camera-to-world calibration.

METHODS

We utilized the precision of surgical navigation systems (NAV) to address the hand-eye calibration problem, thereby localizing the MR camera within a navigated surgical scene. The proposed calibration method was integrated into a representative surgery system and subjected to rigorous testing across various 2D and 3D camera trajectories that simulate surgeon head movements.

RESULTS

The calibration method demonstrated positional errors as low as 0.2 mm in spatial trajectories, with a standard error also at 0.2 mm, underscoring its robustness against camera motion. This accuracy complies with the accuracy and stability requirements essential for surgical applications.

CONCLUSION

The proposed fiducial-based hand-eye calibration method effectively incorporates the accuracy and reliability of surgical navigation systems into MR camera systems used in intraoperative applications. This integration facilitates high precision in surgical navigation, proving critical for enhancing surgical outcomes in mixed-reality-assisted procedures.

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Reduced order modeling and model order reduction for continuum manipulators: an overview

Soft robot's natural dynamics calls for the development of tailored modeling techniques for control. However, the high-dimensional configuration space of the geometrically exact modeling approaches for soft robots, i.e., Cosserat rod and Finite Element Methods (FEM), has been identified as a key obstacle in controller design. To address this challenge, Reduced Order Modeling (ROM), i.e., the approximation of the full-order models, and Model Order Reduction (MOR), i.e., reducing the state space dimension of a high fidelity FEM-based model, are enjoying extensive research. Although both techniques serve a similar purpose and their terms have been used interchangeably in the literature, they are different in their assumptions and implementation. This review paper provides the first in-depth survey of ROM and MOR techniques in the continuum and soft robotics landscape to aid Soft Robotics researchers in selecting computationally efficient models for their specific tasks.

Hyperelastic Modeling and Validation of Hybrid-Actuated Soft Robot with Pressure-Stiffening

Soft robots have gained popularity, especially in intraluminal applications, because their soft bodies make them safer for surgical interventions than flexures with rigid backbones. This study investigates a pressure-regulating stiffness tendon-driven soft robot and provides a continuum mechanics model for it towards using that in adaptive stiffness applications. To this end, first, a central single-chamber pneumatic and tri-tendon-driven soft robot was designed and fabricated. Afterward, the classic Cosserat's rod model was adopted and augmented with the hyperelastic material model. The model was then formulated as a boundary-value problem and was solved using the shooting method. To identify the pressure-stiffening effect, a parameter-identification problem was formulated to identify the relationship between the flexural rigidity of the soft robot and internal pressure. The flexural rigidity of the robot at various pressures was optimized to match theoretical deformation and experiments. The theoretical findings of arbitrary pressures were then compared with the experiment for validation. The internal chamber pressure was in the range of 0 to 40 kPa and the tendon tensions were in the range of 0 to 3 N. The theoretical and experimental findings were in fair agreement for tip displacement with a maximum error of 6.40% of the flexure's length.

Stiffness Adaptation of a Hybrid Soft Surgical Robot for Improved Safety in Interventional Surgery

Minimally invasive instruments are inserted per-cutaneously and are steered toward the desired anatomy. The low stiffness of instruments is an advantage; however, once the target is reached, the instrument usually is required to transmit force to the environment. The main limitation of the constant stiffness is predetermined maneuverability and cap of force transmission. Whereas, a highly flexible device can be safely steered through the body but is not suitable for payload limit, while a highly stiff device can have relatively high loads but cannot be steered in highly tortuous trajectories. To overcome this limitation, an adaptive stiffness soft robot was proposed, and the effects of the chamber pressure on the stiffness of the soft robot were investigated. To this end, a single-chamber pneumatic soft robot with one tendon was designed and fabricated. Afterward, a continuum mechanics model based on the nonlinear Cosserat rod model with hyperelastic material model and large deformation kinematics of the robot was developed. The shooting method solved the model as a boundary value problem with Dirichlet and Neumann boundary conditions. The results of the model showed stiffness adaptation feasibility with simultaneous tendon-driving and pneumatic actuation. Thus, to validate the theoretical findings, a series of experimental studies were performed with pressure in the range of 33 to 44 kPa and tendon tensions in the range of 0 to 2.7 N. The theoretical and experimental results for tip displacement and stiffness showed similar trends with a maximum error of 8.25%.

On the Mathematical Modeling of Slender Biomedical Continuum Robots

The passive, mechanical adaptation of slender, deformable robots to their environment, whether the robot be made of hard materials or soft ones, makes them desirable as tools for medical procedures. Their reduced physical compliance can provide a form of embodied intelligence that allows the natural dynamics of interaction between the robot and its environment to guide the evolution of the combined robot-environment system. To design these systems, the problems of analysis, design optimization, control, and motion planning remain of great importance because, in general, the advantages afforded by increased mechanical compliance must be balanced against penalties such as slower dynamics, increased difficulty in the design of control systems, and greater kinematic uncertainty. The models that form the basis of these problems should be reasonably accurate yet not prohibitively expensive to formulate and solve. In this article, the state-of-the-art modeling techniques for continuum robots are reviewed and cast in a common language. Classical theories of mechanics are used to outline formal guidelines for the selection of appropriate degrees of freedom in models of continuum robots, both in terms of number and of quality, for geometrically nonlinear models built from the general family of one-dimensional rod models of continuum mechanics. Consideration is also given to the variety of actuators found in existing designs, the types of interaction that occur between continuum robots and their biomedical environments, the imposition of constraints on degrees of freedom, and to the numerical solution of the family of models under study. Finally, some open problems of modeling are discussed and future challenges are identified.

Optical Fiber Array Sensor for Force Estimation and Localization in TAVI Procedure: Design, Modeling, Analysis and Validation

Transcatheter aortic valve implantation has shown superior clinical outcomes compared to open aortic valve replacement surgery. The loss of the natural sense of touch, inherited from its minimally invasive nature, could lead to misplacement of the valve in the aortic annulus. In this study, a cylindrical optical fiber sensor is proposed to be integrated with valve delivery catheters. The proposed sensor works based on intensity modulation principle and is capable of measuring and localizing lateral force. The proposed sensor was constituted of an array of optical fibers embedded on a rigid substrate and covered by a flexible shell. The optical fibers were modeled as Euler–Bernoulli beams with both-end fixed boundary conditions. To study the sensing principle, a parametric finite element model of the sensor with lateral point loads was developed and the deflection of the optical fibers, as the determinant of light intensity modulation was analyzed. Moreover, the sensor was fabricated, and a set of experiments were performed to study the performance of the sensor in lateral force measurement and localization. The results showed that the transmitted light intensity decreased up to 24% for an external force of 1 N. Additionally, the results showed the same trend between the simulation predictions and experimental results. The proposed sensor was sensitive to the magnitude and position of the external force which shows its capability for lateral force measurement and localization.

A Soft Robot for Peripheral Lung Cancer Diagnosis and Therapy

Lung cancer is one of the deadliest forms of cancers and is often diagnosed by performing biopsies with the use of a bronchoscope. However, this diagnostic procedure is limited in ability to explore deep into the periphery of the lung where cancer can remain undetected. In this study, we present design, modeling, fabrication, and testing of a one degree of freedom soft robot with integrated diagnostic and interventional capabilities as well as vision sensing. The robot can be deployed through the working channel of commercial bronchoscopes or used as a stand-alone system as it integrates a micro camera to provide vision sensing and controls to the periphery of the lung. The small diameter (2.4 mm) of the device allows navigation in branches deeper in the lung, where current devices have limited reachability. We have performed mechanical characterizations of the robotic platform, including blocked force, maximum bending angle, maximum angular velocity, and workspace, and assessed its performance in in vitro and ex vivo experiments. We have developed a computer vision algorithm, and validated it in in vitro conditions, to autonomously align the robot to a selected branch of the lung and aid the clinician (by means of a graphical user interface) during navigation tasks and to perform robot-assisted stabilization in front of a lesion, with automated tracking and alignment.

Soft Grippers for Automatic Crop Harvesting: A Review

Agriculture 4.0 is transforming farming livelihoods thanks to the development and adoption of technologies such as artificial intelligence, the Internet of Things and robotics, traditionally used in other productive sectors. Soft robotics and soft grippers in particular are promising approaches to lead to new solutions in this field due to the need to meet hygiene and manipulation requirements in unstructured environments and in operation with delicate products. This review aims to provide an in-depth look at soft end-effectors for agricultural applications, with a special emphasis on robotic harvesting. To that end, the current state of automatic picking tasks for several crops is analysed, identifying which of them lack automatic solutions, and which methods are commonly used based on the botanical characteristics of the fruits. The latest advances in the design and implementation of soft grippers are also presented and discussed, studying the properties of their materials, their manufacturing processes, the gripping technologies and the proposed control methods. Finally, the challenges that have to be overcome to boost its definitive implementation in the real world are highlighted. Therefore, this review intends to serve as a guide for those researchers working in the field of soft robotics for Agriculture 4.0, and more specifically, in the design of soft grippers for fruit harvesting robots.

Machine Learning in Arrhythmia and Electrophysiology

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.