Three-Dimensional Modeling and Neural Networks Applied to Dental Trajectory Simulation

The field of dental medicine has made significant advancements in recent decades, with dental professionals developing new techniques and tools to improve the precision and effectiveness of diagnosing and treating oral diseases. One of these tools is the dental articulator, which is used to simulate the movement of the temporomandibular joint, thus allowing for faster and less invasive diagnostic and treatment interventions. Engineering has played a significant role in the development of more precise and efficient orthodontic techniques and tools, such as digital models, 3D printing of dental implants, and the use of different techniques to record dental movements. These advancements have led to greater precision, efficiency, and comfort in dental treatments and have improved patient outcomes. However, obtaining precise dental trajectories remains a challenge due to the limitations of current dental articulators. To address this challenge, a new approach based on 3D modeling and machine learning is proposed to improve the accuracy of dental trajectory prediction. This approach involves modeling a dental articulator using computer-aided design software, parametrizing the model, and obtaining trajectory data based on certain parameters. The data collected will be used to train a machine learning model based on neural networks to predict dental trajectories. This approach has the potential to significantly improve the accuracy of dental trajectory prediction, which could be useful in the planning and execution of dental treatments and the training of dental professionals. Furthermore, this approach could be applied to other medical fields, such as maxillofacial surgery, to improve surgical.