## 2 Materials and Methods
### Validation of Simulator Performance and Model Accuracy  
The protocol for the proposed study received approval from the Institutional Review Board (CUDHIRB 2301002). Performance evaluations are conducted to ensure the smooth execution of the maxillofacial reconstruction surgery model creation process in the simulator. These evaluations are carried out using Unity Profiler, a simulation performance analysis tool. Unity Profiler provides performance data such as frames per second (FPS), resource usage (CPU, GPU, and memory), model rendering and physics engine computation speed, network latency, and UI resource allocation. FPS and resource usage (CPU, GPU, and memory) were selected as performance evaluation metrics, as they are closely related to the smooth operation of the simulator. FPS serves as an indicator of the simulator's graphic processing speed, while resource usage measures the performance load on the CPU and GPU during simulator operation. Memory usage represents the amount of memory resources required for tasks such as model generation, interaction, and resection. These metrics help evaluate the simulator's efficiency in processing and graphic performance as well as computing resource utilization. Additionally, since model resection and interaction are critical functions for generating reconstruction models during simulator operation, resection time and interaction recognition accuracy are also utilized as performance evaluation metrics. Resection time represents the time required for model resection, while interaction recognition accuracy indicates the model recognition rate through the controller's raycasting. These metrics are measured through tests conducted in a fixed PC environment. During the tests, the simulator's processes (model visualization, interaction, manipulation, and resection) are performed over a set period, and the metric values are recorded. The average values are then calculated to assess the simulator's performance. The specifications of the PC used for performance evaluation are shown in Table 2, and the selected performance metrics and evaluation methods are shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline
**Hardware** & **Specification** \\ \hline CPU & i9-12900 K, 16 Cores, 24 Threads, 5.20 GHz \\ GPU & GeForce RTX 3080, 12 GB \\ Memory & DDR4-3200, 16 GB per module \(\times\) 2 \\ Storage & 870 EVO, 2 TB per drive \(\times\) 2 \\ OS & Window 10 Education \\ \hline \end{tabular}
\end{table} 
Table 2: PC specifications used for performance evaluation. Figure 7: Maxillofacial reconstruction model generation process using the proposed method: (**a**) model selection; (**b**) model visualization; (**c**) maxillofacial model manipulation; (**d**) maxillofacial resection plane creation; (**e**) maxillofacial model resection; (**f**) positioning the iliac bone model to the resected mandible area; (**g**) iliac resection plane creation; (**h**) iliac bone model resection and fine adjustment of the resected model; and (**i**) mandible reconstruction model creation with iliac bone. Model accuracy validation is performed to confirm the validity of the implemented maxillofacial reconstruction surgery simulator. This validation assesses the accuracy of the maxillofacial reconstruction model generated through the simulator. Through this, the accuracy of maxillofacial reconstruction, the reliability of the simulator, and the consistency of surgical planning can be confirmed. To validate the accuracy of the simulator-generated maxillofacial reconstruction model, the open-source software CloudCompare v2.14 is used. The comparative evaluation is conducted based on CT data from 10 patients who underwent maxillofacial reconstruction surgery using the iliac bone, and the comparison method involves shape comparison between models created by a maxillofacial surgeon using both a medical CAD tool and the simulator.