# Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery
## 4 Discussion  
Advanced training techniques can enhance the specific skills required for minimally invasive treatments. This work presents a simulator that offers a realistic training environment for minimally invasive pancreatic surgery. The system comprises a parallel robot, the Omega 7 haptic device by Force Dimension, the HoloLens 2 device, and an AI network, all integrated within the Unity 3D engine. Parallel robots are known for their accuracy and stiffness compared to serial architectures [27; 28; 29]. Parallel robots are also more challenging to implement in engine graphics. The Omega 7, a well-known haptic device, was chosen primarily for its availability. The HoloLens 2 is a popular choice for implementing AR technology due to its high-quality visualization, comfort, ergonomics, and spatial mapping capabilities. However, its price, limited battery life, and potential future lack of support may hinder its use in commercially available surgical training systems, which must be considered in future development. An AI-based force prediction system is also included into the simulator, enabling real-time haptic feedback without the use of physical sensors. The system predicts forces with a maximum deviation of 0.045 N and an average variance of \(\pm\)0.02 N, according to the results. With an average delay of 65 ms, haptic feedback technology is appropriate for real-time applications. Figure 12: Evaluation of pragmatic quality, hedonic quality, and overall system performance. Figure 13: UEQ analysis of the entire system. While the simulator demonstrates promising results, there are areas where further enhancements could improve its performance. One area to address is the sensitivity of the AI model to lighting conditions. The holograms projected by the HoloLens 2 are more vivid and easier to see in low light, as there is less interference from surrounding light, which makes the system more effective in such conditions than in natural light. Additionally, the current force range of 1 N is tailored to a specific organ, providing an opportunity to expand its applicability to a broader range of scenarios. Ultimately, when successive forces are applied without allowing the tissue to return to its original shape, a hysteresis effect is observed. This highlights the need for further refinement to account for time-dependent tissue behaviors, enhancing the realism and functionality of the simulator. A UEQ analysis revealed strong performance in novelty, perspicuity, and attractiveness, indicating that the system is innovative, user-friendly, and aesthetically pleasing. Stimulation also scored well, suggesting that the event was enjoyable. However, lower scores in dependability and efficiency highlight areas for improvement, especially in system reliability. Compared to other simulators in the scientific literature, such as the da Vinci Skills Simulator (dVSS) and the Robotic Surgical Simulator (RoSS), this system addresses specific gaps. It combines visual data and AI-based force estimation, which eliminates the need for extra sensors, unlike conventional simulators relying on physical sensors and predetermined activities for haptic feedback, which leads to cost-effectiveness and versatility. The simulator's modular architecture suggests its potential integration into more extensive surgical simulation frameworks. The AI technology and modular robot might improve existing systems by augmenting the training realism for less invasive treatments. From an economic perspective, the simulator represents a more cost-efficient option for training facilities, since it does not need actual sensors and utilizes a relatively inexpensive AR device. Artificial intelligence-driven force prediction reduces reliance on costly gear, possibly enhancing accessibility to surgical training instruments. Further improvements will be focused on increasing the database, including different instruments for a better generalization of the simulator. Also, advanced biomechanical models could improve the realism of tissue behavior.