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Background The surgical evaluation of pediatric liver tumors is challenging, especially in difficult cases that require precise surgery. Three-dimensional visualization (3D) based on two-dimensional CT (2D) has been widely used. However, virtual surgical simulation (VS), which can provide more procedural details for specific patients, is worthy of further exploration. Methods Six pediatric liver tumor patients with the surgical difficulty increasing sequentially were selected. Recruited pediatric surgeons (15 junior and 15 senior) guided professional technicians in performing surgical evaluation using 2D, 3D, and VS sequentially. The patient-specific VS based on 3D can be constructed within the clinically permissible time window to meet clinical needs. Results For objective analysis, the scores in 3D Group and VS Group were significantly higher than those in 2D Group ( P  < 0.0001), and there was also a statistically significant difference between 3D Group and VS Group ( P  < 0.0001). As the difficulty increased, the 3D Group and VS Group consistently maintained higher scores. The scores of Junior Group using 2D were significantly lower than those of Senior Group ( P  < 0.0001), but there was no significant statistical difference in the scores of using 3D and VS. For subjective assessment, the scores of 3D Group and VS Group were significantly higher than those of 2D Group ( P  < 0.0001). VS was more aligned with clinical reality compared to 3D. Conclusion 3D and VS offer significant advantages in surgical evaluation compared with 2D, particularly for difficult cases and junior surgeons. The novel perspective and realistic experience provided by VS have attracted attention, and future research will further validate its potential clinical value in preoperative rehearsals and advanced training.

The aims of the research were to evaluate the effectiveness of the application of nanostructured products on Volterra calcarenite stone and to define the experimental conditions and procedures of accelerated aging tests, able to simulate different degradation on the studied lithotype. The work focused on methods of performing accelerated aging tests in order to simulate different effects of environmental decay involving stone used on a historical site. The rock samples were examined before and after three treatment types: cyclic salt spray chamber, cycles of freezing–thawing and cycles of thermal shock. After each artificial aging cycle, changes in appearance were noted and chemical and physical properties were measured so that the differences between untreated and treated samples could be compared. After applying nanostructured products on the sample surfaces, and assessing the effects of the accelerated aging, the protective performance of the coatings was evaluated using the contact angle test to evaluate the surface hygroscopicity. Moreover, scanning electron microscope (SEM-EDS) analysis was performed before and after each application of nanostructured coating to evaluate changes in the surface morphology. Results demonstrated that Panchina stone showed a high durability to the aging tests, and artificial degradation effects were not largely visible. The nanostructured products seem to be suitable for stone protection by virtue of their good compatibility and effectiveness.

Robotic surgery promises enhanced precision and adaptability over traditional surgical methods. It also offers the possibility of automating surgical interventions, resulting in reduced stress on the surgeon, better surgical outcomes, and lower costs. Cholecystectomy, the removal of the gallbladder, serves as an ideal model procedure for automation due to its distinct and well-contrasted anatomical features between the gallbladder and liver, along with standardized surgical maneuvers. Dissection is a frequently used subtask in cholecystectomy where the surgeon delivers the energy on the hook to detach the gallbladder from the liver. Hence, dissection along tissue boundaries is a good candidate for surgical automation. For the da Vinci surgical robot to perform the same procedure as a surgeon automatically, it needs to have the ability to (1) recognize and distinguish between the two different tissues (e.g. the liver and the gallbladder), (2) understand where the boundary between the two tissues is located in the 3D workspace, (3) locate the instrument tip relative to the boundary in the 3D space using visual feedback, and (4) move the instrument along the boundary. This paper presents a novel framework that addresses these challenges through AI-assisted image processing and vision-based robot control. We also present the ex-vivo evaluation of the automated procedure on chicken and pork liver specimens that demonstrates the effectiveness of the proposed framework.

In recent years, the potential applications of machine learning to Minimally Invasive Surgery (MIS) have spurred interest in data sets that can be used to develop data-driven tools. This paper introduces a novel dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers, utilizing the da Vinci Research Kit (dVRK). Unlike current datasets, ours bridges a critical gap by offering not only full kinematic data but also capturing all pedal inputs used during the procedure and providing a time-stamped record of the endoscope's movements. Contributed by seven surgeons, this data set introduces a new dimension to surgical robotics research, allowing the creation of advanced models for automating console functionalities. Our work addresses the existing limitation of incomplete recordings and imprecise kinematic data, common in other datasets. By introducing two models, dedicated to predicting clutch usage and camera activation, we highlight the dataset's potential for advancing automation in surgical robotics. The comparison of methodologies and time windows provides insights into the models' boundaries and limitations.

Robotic surgery has reached a high level of maturity and has become an integral part of standard surgical care. However, existing surgeon consoles are bulky, take up valuable space in the operating room, make surgical team coordination challenging, and their proprietary nature makes it difficult to take advantage of recent technological advances, especially in virtual and augmented reality. One potential area for further improvement is the integration of modern sensory gloves into robotic platforms, allowing surgeons to control robotic arms intuitively with their hand movements. We propose one such system that combines an HTC Vive tracker, a Manus Meta Prime 3 XR sensory glove, and SCOPEYE wireless smart glasses. The system controls one arm of a da Vinci surgical robot. In addition to moving the arm, the surgeon can use fingers to control the end-effector of the surgical instrument. Hand gestures are used to implement clutching and similar functions. In particular, we introduce clutching of the instrument orientation, a functionality unavailable in the da Vinci system. The vibrotactile elements of the glove are used to provide feedback to the user when gesture commands are invoked. A qualitative and quantitative evaluation has been conducted that compares the current device with the dVRK console. The system is shown to have excellent tracking accuracy, and the new interface allows surgeons to perform common surgical training tasks with minimal practice efficiently.