# Development of an Augmented Reality Surgical Trainer for Minimally Invasive Pancreatic Surgery
## 3 Results  
The simulator's prediction accuracy was evaluated by applying known forces in Unity and comparing them to the forces predicted by the AI model (Figure 8). For each lighting condition (low, medium, and high brightness), forces ranging from 0.1 N to 1.0 N were incrementally applied, and the predicted forces were recorded. The results indicated an average deviation of \(\pm\)0.02 N and a maximum deviation of 0.045 N, which means that for an applied force of 0.5 N, the model consistently predicted forces within the range of [0.48 N, 0.54 N] across all lighting conditions. In HoloLens testing, similar validation was performed by simulating force interactions under two lighting environments: natural daylight and low light (Figure 9). Figure 10 displays plots comparing predicted force (N) to applied force (N), with a diagonal line representing a perfect correlation between the two. In the Unity plot, data points corresponding to low, medium, and high brightness levels (shown in red, green, and blue) remain consistently close to the diagonal, indicating stable accuracy across different lighting conditions. Conversely, the HoloLens plot, with gray markers for natural daylight and red markers for low light, shows that the red (low-light) data points are positioned closer to the diagonal, suggesting improved prediction accuracy in low-light environments. Figure 8: Force prediction in Unity. Figure 7: Augmented reality with ATHENA parallel robot integrated into the operating room. This indicates that while Unity exhibits consistent performance regardless of brightness, the HoloLens model performs best under low-light conditions, with slightly reduced accuracy observed in natural daylight. The simulator was also tested to observe its reaction to unknown data, such as a different instrument compared to what the model was trained on. The results showed that the deviation from the predicted values was slightly increased. Following ten repetitions, the average deviation was 0.025 N, indicating that the model learned the deformation feature and is not overly reliant on details such as a different instrument. The simulator was also tested to observe what happens when the instrument applies another force while the tissue has not fully returned to its original form yet. This test was repeated 20 times with different forces, and the results indicated an average deviation of 0.08 Newtons, suggesting that, at some level, the model offers acceptable precision, but a hysteresis effect can be observed. Figure 10: Comparison of force prediction performance: Unity-based simulation (**a**) with varying intensity levels (**b**) and HoloLens-based simulation (right) under different lighting conditions (natural daylight and low light). Figure 9: The AR environment highlighting the force prediction in HoloLens: the hologram of the pancreas is on the left and the instrument pressing on the tissue and the haptic device are on the right. The time interval between force application and value perception by the Omega 7 haptic device was analyzed to assess the simulator's real-time performance. To consistently measure the feedback propagation time, multiple force applications on the tissue were simulated; the feedback delay was calculated by measuring the delay 10 times for each force applied and then averaging these 10 measurements to obtain a more reliable value for the delay. The histogram (Figure 11) shows delays ranging from 60 ms to 72 ms, with the most frequent values being around 66 ms. This suggests that the feedback delay is constant, presenting just minor fluctuations. The average response time of 66 ms is considered adequate for real-time simulations [25]. The threshold for real-time simulations confirms that the haptic feedback is properly synchronized with the force application and that the system provides reliable, real-time interaction with the user. The collaborative aspect of the robot was tested by having users manually move the robot's end-effector along a predefined trajectory.