Browsing by Author "Boneva A.T."

Now showing 1 - 1 of 1
  • No Thumbnail Available
    Item
    Training of Surgical Skills by a 3D Augmented Liver Model Response During Instrument Interactions Simulation
    (2025-09-30) Ivanova V.; Vasilev P.V.; Boneva A.T.
    Background and Objective: In recent years, interest in surgical robotics simulation has grown significantly, particularly among trainee surgeons. This trend is driven by the demand for cost-effective training solutions, improved surgical outcomes, and reduced training times. Simulations also play a vital role in the design and testing of surgical instruments, enabling analysis of static and dynamic loads and optimization of tool–tissue interactions. However, because of the complex nature of soft tissue deformation during surgical procedures, developing realistic and effective simulations remains a challenge. This study focuses on modeling liver responses during tool–tissue interactions in laparoscopic surgery. Building on prior research in surgical robotics, the goal is to develop a personalized training platform that enhances the skills of surgical personnel without the need for live human or animal subjects. Materials and Methods: The study begins by analyzing the motion of a tactile surgical instrument interacting with tissue. Direct kinematics is used to enable remote control of surgical robots by the lead surgeon. To improve control accuracy, systematic positional errors are introduced into the control links. A simulation program is developed to define the operational workspace and potential tool actions. Movement within this space is controlled by four motors connected to transmission mechanisms. Analytical models of these mechanisms are used to optimize performance under defined constraints. In addition, a training simulation program (TSP) is created to model liver responses during tool–tissue interactions. This program visualizes the 3D behavior of organs using physical material properties and simulates collisions between solids. The Unity Game Engine is used to generate animations compatible with both standard and VR/AR environments. Results: Experimental data involving various laparoscopic instrument tips and biological tissues are stored in a MySQL database. These data can be accessed via local workstations, institutional servers, or cloud-based platforms. Users can also store their simulation data on mobile devices or processor cards. Conclusion: This study presents a comprehensive approach to developing a surgical training system that simulates realistic tool–tissue interactions. The findings contribute to the advancement of minimally invasive surgical education by enabling personalized, data-driven training experiences. The proposed system offers a scalable and ethical alternative to traditional training methods, with potential applications in both academic and clinical settings. The simulation programs effectively transferred acquired skills to real-world scenarios, demonstrating the system’s potential for enhancing surgical training.