Artificial intelligence guided occlusion reconstruction in nonoccluding CBCT: A validation study

Comprehensive Summary

This study in the Journal of Prosthetic Dentistry looks at how artificial intelligence (AI) can be used to create occlusal relationships from cone beam computerized tomographic images in scenarios where there are no occlusal contacts or incomplete occlusal contacts. To achieve this, the authors employed a machine learning algorithm based on datasets with known occlusal relationships to predict accurate occlusal reconstruction and positioning of maxillary and mandibular bones through isolated CBCT images. The proposed solution tackles an important problem associated with digital dentistry in which the information from the use of CBCT images alone is not sufficient for creating occlusal relationships. The accuracy of the model was evaluated using a quantitative method against reference standards, and results were found to be highly accurate in reproducing occlusal relationships.

Outcomes and Implications

The incorporation of AI-based occlusion reconstruction into dental practice will significantly impact the fields of prosthodontics, implantology, and full-mouth rehabilitation. The application of virtual articulation based on CBCT imaging without taking any bite registration or making physical casts would be extremely beneficial for the efficiency of this procedure. Increased accuracy of occlusal reconstruction might result in improved prosthetic fit and higher success rates while minimizing chair-side manipulation. In addition, the described technique would prove to be particularly useful when dealing with edentulous individuals who experience problems with their occlusion, making conventional record-taking impossible. Another advantage of applying AI is that it could minimize operator error during occlusal reconstruction, thus improving the consistency and reliability of the procedure. Thus, this advancement would facilitate a transition to a completely digital and data-based dental industry.