In their recent study published in the World Journal of Clinical Cases, the article found that minimally invasive laparoscopic surgery under general anesthesia demonstrates superior efficacy and safety compared to traditional open surgery for early ovarian cancer patients. This editorial discusses the integration of machine learning in laparoscopic surgery, emphasizing its transformative potential in improving patient outcomes and surgical precision. Machine learning algorithms analyze extensive datasets to optimize procedural techniques, enhance decision-making, and personalize treatment plans. Advanced imaging modalities like augmented reality and real-time tissue classification, alongside robotic surgical systems and virtual reality simulations driven by machine learning, enhance imaging and training techniques, offering surgeons clearer visualization and precise tissue manipulation. Despite promising advancements, challenges such as data privacy, algorithm bias, and regulatory hurdles need addressing for the responsible deployment of machine learning technologies. Interdisciplinary collaborations and ongoing technological innovations promise further enhancement in laparoscopic surgery, fostering a future where personalized medicine and precision surgery redefine patient care.

The absence of force feedback (FFB) is considered a technical limitation in robotic-assisted surgery (RAS). This pre-clinical study aims to evaluate the forces applied to tissues using a novel integrated FFB technology, which allows surgeons to sense forces exerted at the instrument tips.

Twenty-eight surgeons with varying experience levels employed FFB instruments to perform three robotic-assisted surgical tasks, including retraction, dissection, and suturing, on inanimate or ex-vivo models, while the instrument sensors recorded and conveyed the applied forces to the surgeon hand controllers of the robotic system. Generalized Estimating Equations (GEE) models were used to analyze the mean and maximal forces applied during each task with the FFB sensor at the "Off" setting compared to the "High" sensitivity setting for retraction and to the "Low", "Medium", and "High" sensitivity settings for dissection and suturing. Sub-analysis was also performed on surgeon experience levels.

The use of FFB at any of the sensitivity settings resulted in a significant reduction in both the mean and maximal forces exerted on tissue during all three robotic-assisted surgical tasks (p < 0.0001). The maximal force exerted, potentially associated with tissue damage, was decreased by 36%, 41%, and 55% with the use of FFB at the "High" sensitivity setting while performing retraction, dissection, and interrupted suturing tasks, respectively. Further, the use of FFB resulted in substantial reductions in force variance during the performance of all three types of tasks. In general, reductions in mean and maximal forces were observed among surgeons at all experience levels. The degree of force reduction depends on the sensitivity setting selected and the types of surgical tasks evaluated.

Our findings demonstrate that the utilization of FFB technology integrated in the robotic surgical system significantly reduced the forces exerted on tissue during the performance of surgical tasks at all surgeon experience levels. The reduction in the force applied and a consistency of force application achieved with FFB use, could result in decreases in tissue trauma and blood loss, potentially leading to better clinical outcomes in patients undergoing RAS. Future studies will be important to determine the impact of FFB instruments in a live clinical environment.

Cardiovascular disease is becoming the leading cause of human mortality. In order to address this, flexible continuum robots have emerged as a promising solution for miniaturizing and automating vascular interventional equipment for diagnosing and treating cardiovascular diseases. However, existing continuum robots used for vascular intervention face challenges such as large cross-sectional sizes, inadequate driving force, and lack of navigation control, preventing them from accessing cerebral blood vessels or capillaries for medical procedures. Additionally, the complex manufacturing process and high cost of soft continuum robots hinder their widespread clinical application. In this study, we propose a thermally drawn-based microtubule soft continuum robot that overcomes these limitations. The proposed robot has cross-sectional dimensions several orders of magnitude smaller than the smallest commercially available conduits, and it can be manufactured without any length restrictions. By utilizing a driving strategy based on liquid kinetic energy advancement and external magnetic field for steering, the robot can easily navigate within blood vessels and accurately reach the site of the lesion. This innovation holds the potential to achieve controlled navigation of the robot throughout the entire blood vessel, enabling in situ diagnosis and treatment of cardiovascular diseases.

The absence of an effective gripping force feedback mechanism in minimally invasive surgical robot systems impedes physicians' ability to accurately perceive the force between surgical instruments and human tissues during surgery, thereby increasing surgical risks. To address the challenge of integrating force sensors on minimally invasive surgical tools in existing systems, a clamping force prediction method based on mechanical clamp blade motion parameters is proposed. The interrelation between clamping force, displacement, compression speed, and the contact area of the clamp blade indenter was analyzed through compression experiments conducted on isolated pig kidney tissue. Subsequently, a prediction model was developed using a backpropagation (BP) neural network optimized by the Sparrow Search Algorithm (SSA). This model enables real-time prediction of clamping force, facilitating more accurate estimation of forces between instruments and tissues during surgery. The results indicate that the SSA-optimized model outperforms traditional BP networks and genetic algorithm-optimized (GA) BP models in terms of both accuracy and convergence speed. This study not only provides technical support for enhancing surgical safety and efficiency, but also offers a novel research direction for the design of force feedback systems in minimally invasive surgical robots in the future.

Stereopsis may be an advantage of robotic surgery. Perceived robotic ergonomic advantages in visualisation include better exposure, three-dimensional vision, surgeon camera control, and line of sight screen location. Other ergonomic factors relating to visualisation include stereo-acuity, vergence-accommodation mismatch, visual-perception mismatch, visual-vestibular mismatch, visuospatial ability, visual fatigue, and visual feedback to compensate for lack of haptic feedback. Visual fatigue symptoms may be related to dry eye or accommodative/binocular vision stress. Digital eye strain can be measured by questionnaires and objective tests. Management options include treatment of dry eye, correction of refractive error, and management of accommodation and vergence anomalies. Experienced robotic surgeons can use visual cues like tissue deformation and surgical tool information as surrogates for haptic feedback.

Effective force modulation during tissue manipulation is important for ensuring safe, robot-assisted, minimally invasive surgery (RMIS). Strict requirements for in vivo applications have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. Due to this trade-off, there are no commercial, off-the-shelf, 3-degrees-of-freedom (3DoF) force sensors for RMIS available to researchers. This makes it challenging to develop new approaches to indirect sensing and haptic feedback for bimanual telesurgical manipulation. We present a modular 3DoF force sensor that integrates easily with an existing RMIS tool. We achieve this by relaxing biocompatibility and sterilizability requirements and by using commercial load cells and common electromechanical fabrication techniques. The sensor has a range of ±5 N axially and ±3 N laterally with errors of below 0.15 N and maximum errors below 11% of the sensing range in all directions. During telemanipulation, a pair of jaw-mounted sensors achieved average errors below 0.15 N in all directions. It achieved an average grip force error of 0.156 N. The sensor is for bimanual haptic feedback and robotic force control in delicate tissue telemanipulation. As an open-source design, the sensors can be adapted to suit other non-RMIS robotic applications.

In recent years, robotic minimally invasive surgery has transformed many types of surgical procedures and improved their outcomes. Implementing effective haptic feedback into a teleoperated robotic surgical system presents a significant challenge due to the trade-off between transparency and stability caused by system communication time delays. In this paper, these time delays are mitigated by implementing an environment estimation and force prediction methodology into an experimental robotic minimally invasive surgical system. At the slave, an exponentially weighted recursive least squares (EWRLS) algorithm estimates the respective parameters of the Kelvin-Voigt (KV) and Hunt-Crossley (HC) force models. The master then provides force feedback by interacting with a virtual environment via the estimated parameters. Palpation experiments were conducted with the slave in contact with polyurethane foam during human-in-the-loop teleoperation. The experimental results indicated that the prediction RMSE of error between predicted master force feedback and measured slave force was reduced to 0.076 N for the Hunt-Crossley virtual environment, compared to 0.356 N for the Kelvin-Voigt virtual environment and 0.560 N for the direct force feedback methodology. The results also demonstrated that the HC force model is well suited to provide accurate haptic feedback, particularly when there is a delay between the master and slave kinematics. Furthermore, a haptic feedback approach that incorporates environment estimation and force prediction improve transparency during teleoperation. In conclusion, the proposed bilateral master-slave robotic system has the potential to provide transparent and stable haptic feedback to the surgeon in surgical robotics procedures.

There are many definitions for the concept of a robot, perhaps too many; it has even been said that we do not know how to define them, but when we see a robot, we identify it [...].

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human-Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.