Semi-Autonomous Electrosurgery for Tumor Resection Using a Multi-Degree of Freedom Electrosurgical Tool and Visual Servoing

Histological assessment of thermal damage in porcine muscle induced by monopolar electrosurgical cutting devices during manual and robotic testing

Surgical cutting with electrosurgical tools facilitates tissue dissection and vessel sealing, preventing blood loss. The extent of tissue necrosis due to temperature elevations is dependent on the cutting technique, device design, coating properties and power settings, but the influence of these parameters is not fully understood. Here we conduct a comprehensive comparative analysis of thermal damage comparing (1) manual user-controlled and robotic electrosurgical cutting approaches for (2) varying electrodes and coatings, and power settings. We demonstrate that ceramic coating significantly enhanced cutting performance and cut quality and reduced lateral thermal damage, by 86.15% at 35 W and 65% at 50 W respectively. We provide quantitative assessment of the influence of surgical variability on thermal damage, comparing robotic and manual electrosurgical cutting. Robotic cutting with one ceramic electrosurgical coated device reduced thermal damage (midline - 47.42%, lateral - 33.06%), whereas for the other coated electrode the thermal spread increased (midline - 66.57%, lateral -245.72). Thus, thermal damage performance was strongly influenced by surgical variability and the specific characteristics of each device. Together, these results provide an enhanced understanding of potential mechanisms determining electrosurgical outcomes. Understanding of these interdependencies and mechanisms of action linked to a specific electrosurgical system is essential for successful tissue resection.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Sensor-Based Automated Detection of Electrosurgical Cautery States

In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools' location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery-robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.

Online Hypermodel-based Path Planning for Feedback Control of Tissue Denaturation in Electrosurgical Cutting

The first closed-loop control of electrosurgical power satisfying a specified tissue damage bound along the desired tissue dissection path is presented. The damage is represented by the 82 °C isotherm corresponding to the admissible tissue denaturation front position in relation to that of the cutting probe tip. The front location is assessed in real time through the infrared temperature readings of the 40 °C isotherm tightly related to the emerging denaturing patch size around the moving probe tip. A control-oriented denaturing hypermodel and its recasting into a form amenable for use in a moving-horizon locally linear model predictive control law are presented. The optimal control action is determined by solving a compound model predictive control problem that targets a number of active one-dimensional domains. This model is obtained from an offline trained nonlinear autoregressive model with exogenous input. To enforce the safety constraints, a supervisor system precedes the path planning control law. This system prevents excessive denaturation by excluding certain system moves, and determines system termination conditions. We experimentally demonstrate the system's performance in two different line-cutting tasks on ex vivo porcine tissue with a desired denaturation front.

Supervised Autonomous Electrosurgery for Soft Tissue Resection

Surgical resection is the current clinical standard of care for treating squamous cell carcinoma. Maintaining an adequate tumor resection margin is the key to a good surgical outcome, but tumor edge delineation errors are inevitable with manual surgery due to difficulty in visualization and hand-eye coordination. Surgical automation is a growing field of robotics to relieve surgeon burdens and to achieve a consistent and potentially better surgical outcome. This paper reports a novel robotic supervised autonomous electrosurgery technique for soft tissue resection achieving millimeter accuracy. The tumor resection procedure is decomposed to the subtask level for a more direct understanding and automation. A 4-DOF suction system is developed, and integrated with a 6-DOF electrocautery robot to perform resection experiments. A novel near-infrared fluorescent marker is manually dispensed on cadaver samples to define a pseudotumor, and intraoperatively tracked using a dual-camera system. The autonomous dual-robot resection cooperation workflow is proposed and evaluated in this study. The integrated system achieves autonomous localization of the pseudotumor by tracking the near-infrared marker, and performs supervised autonomous resection in cadaver porcine tongues (N=3). The three pseudotumors were successfully removed from porcine samples. The evaluated average surface and depth resection errors are 1.19 and 1.83mm, respectively. This work is an essential step towards autonomous tumor resections.

Vaginal Cuff Closure With Dual-Arm Robot and Near-Infrared Fluorescent Sutures

This paper presents a dual-arm suturing robot. We extend the Smart Tissue Autonomous Robot (STAR) with a second robot manipulator, whose purpose is to manage loose suture thread, a task that was previously executed by a human assistant. We also introduce novel near-infrared fluorescent (NIRF) sutures that are automatically segmented and delimit the boundaries of the suturing task. During ex-vivo experiments of porcine models, our results demonstrate that this new system is capable of outperforming human surgeons in all but one metric for the task of vaginal cuff closure (porcine model) and is more consistent in every aspect of the task. We also present results to demonstrate that the system can perform a vaginal cuff closure during an in-vivo experiment (porcine model).

A learning robot for cognitive camera control in minimally invasive surgery

Background

We demonstrate the first self-learning, context-sensitive, autonomous camera-guiding robot applicable to minimally invasive surgery. The majority of surgical robots nowadays are telemanipulators without autonomous capabilities. Autonomous systems have been developed for laparoscopic camera guidance, however following simple rules and not adapting their behavior to specific tasks, procedures, or surgeons.

Methods

The herein presented methodology allows different robot kinematics to perceive their environment, interpret it according to a knowledge base and perform context-aware actions. For training, twenty operations were conducted with human camera guidance by a single surgeon. Subsequently, we experimentally evaluated the cognitive robotic camera control. A VIKY EP system and a KUKA LWR 4 robot were trained on data from manual camera guidance after completion of the surgeon’s learning curve. Second, only data from VIKY EP were used to train the LWR and finally data from training with the LWR were used to re-train the LWR.

Results

The duration of each operation decreased with the robot’s increasing experience from 1704 s ± 244 s to 1406 s ± 112 s, and 1197 s. Camera guidance quality (good/neutral/poor) improved from 38.6/53.4/7.9 to 49.4/46.3/4.1% and 56.2/41.0/2.8%.

Conclusions

The cognitive camera robot improved its performance with experience, laying the foundation for a new generation of cognitive surgical robots that adapt to a surgeon’s needs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00464-021-08509-8.

"Hey Siri! Perform a type 3 hysterectomy. Please watch out for the ureter!" What is autonomous surgery and what are the latest developments?

As a result of major advances in deep learning algorithms and computer processing power, there have been important developments in the fields of medicine and robotics. Although fully autonomous surgery systems where human impact will be minimized are still a long way off, systems with partial autonomy have gradually entered clinical use. In this review, articles on autonomous surgery classified and summarized, with the aim of informing the reader about questions such as "What is autonomic surgery?" and in which areas studies are progressing.

Optical Coherence Tomography-Guided Robotic Ophthalmic Microsurgery via Reinforcement Learning from Demonstration

Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. We demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This work shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT guided robotic ophthalmic surgery.

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H_∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

Supervised Autonomous Electrosurgery via Biocompatible Near-Infrared Tissue Tracking Techniques

Autonomous robotic surgery systems aim to improve patient outcomes by leveraging the repeatability and consistency of automation and also reducing human induced errors. However, intraoperative autonomous soft tissue tracking and robot control still remains a challenge due to the lack of structure, and high deformability of such tissues. In this paper, we take advantage of biocompatible Near-Infrared (NIR) marking methods and develop a supervised autonomous 3D path planning, filtering, and control strategy for our Smart Tissue Autonomous Robot (STAR) to enable precise and consistent incisions on complex 3D soft tissues. Our experimental results on cadaver porcine tongue samples indicate that the proposed strategy reduces surface incision error and depth incision error by 40.03% and 51.5%, respectively, compared to a teleoperation strategy via da Vinci. Furthermore, compared to an autonomous path planning method with linear interpolation between the NIR markers, the proposed strategy reduces the incision depth error by 48.58% by taking advantage of 3D tissue surface information.

A Confidence-Based Shared Control Strategy for the Smart Tissue Autonomous Robot (STAR)

Autonomous robotic assisted surgery (RAS) systems aim to reduce human errors and improve patient outcomes leveraging robotic accuracy and repeatability during surgical procedures. However, full automation of RAS in complex surgical environments is still not feasible and collaboration with the surgeon is required for safe and effective use. In this work, we utilize our Smart Tissue Autonomous Robot (STAR) to develop and evaluate a shared control strategy for the collaboration of the robot with a human operator in surgical scenarios. We consider 2D pattern cutting tasks with partial blood occlusion of the cutting pattern using a robotic electrocautery tool. For this surgical task and RAS system, we i) develop a confidence-based shared control strategy, ii) assess the pattern tracking performances of manual and autonomous controls and identify the confidence models for human and robot as well as a confidence-based control allocation function, and iii) experimentally evaluate the accuracy of our proposed shared control strategy. In our experiments on porcine fat samples, by combining the best elements of autonomous robot controller with complementary skills of a human operator, our proposed control strategy improved the cutting accuracy by 6.4%, while reducing the operator work time to 44 % compared to a pure manual control.

Updates in Urologic Robot Assisted Surgery

Modern robotics is an advanced minimally invasive technology with the advantages of wristed capability, three-dimensional optics, and tremor filtration compared with conventional laparoscopy. Urologists have been early adopters of robotic surgical technology: robotics have been used in urologic oncology for more than 20 years and there has been an increasing trend for utilization in benign urologic pathology in the last couple of years. The continuing development and interest in robotics are aimed at surgical efficiency as well as patient outcomes. However, despite its advantages, improvements in haptics, system size, and cost are still desired. This article explores the current use of robotics in urology as well as future improvements on the horizon.

Semi-Autonomous Laparoscopic Robotic Electro-surgery with a Novel 3D Endoscope

This paper reports a robotic laparoscopic surgery system performing electro-surgery on porcine cadaver kidney, and evaluates its accuracy in an open loop control scheme to conduct targeting and cutting tasks guided by a novel 3D endoscope. We describe the design and integration of the novel laparoscopic imaging system that is capable of reconstructing the surgical field using structured light. A targeting task is first performed to determine the average positioning error of the system as guided by the laparoscopic camera. The imaging system is then used to reconstruct the surface of a porcine cadaver kidney, and generate a cutting trajectory with consistent depth. The paper concludes by using the robotic system in open loop control to cut this trajectory using a multi degree of freedom electro-surgical tool. It is demonstrated that for a cutting depth of 3 mm, the robotic surgical system follows the trajectory with an average depth of 2.44 mm and standard deviation of 0.34 mm. The average positional accuracy of the system was 2.74±0.99 mm.