Enhanced diagnostic pipeline for maxillary sinus-maxillary molars relationships: a novel implementation of Detectron2 with faster R-CNN R50 FPN 3x on CBCT images.

BACKGROUND

The anatomical relationship between the maxillary sinus and maxillary molars is critical for planning dental procedures such as tooth extraction, implant placement and periodontal surgery.

METHODS

This study presents a novel artificial intelligence-based approach for the detection and classification of these anatomical relationships in cone beam computed tomography (CBCT) images. The model, developed using advanced image recognition technology, can automatically detect the relationship between the maxillary sinus and adjacent molars with high accuracy.

RESULTS

The artificial intelligence algorithm used in our study provided faster and more consistent results compared to traditional manual evaluations, reaching 89% accuracy in the classification of anatomical structures.

CONCLUSIONS

With this technology, clinicians will be able to more accurately assess the risks of sinus perforation, oroantral fistula and other surgical complications in the maxillary posterior region preoperatively. By reducing the workload associated with CBCT analysis, the system accelerates clinicians' diagnostic process, improves treatment planning and increases patient safety. It also has the potential to assist in the early detection of maxillary sinus pathologies and the planning of sinus floor elevation procedures. These findings suggest that the integration of AI-powered image analysis solutions into daily dental practice can improve clinical decision-making in oral and maxillofacial surgery by providing accurate, efficient and reliable diagnostic support.