Pilot Study: Algorithm-Based Assessment of Maxillary Sinus Volume in Zygomatic and Pterygoid Implant Planning

Background/Objetives: Zygomatic implants are an effective solution for the prosthetic rehabilitation of atrophic maxillae, but their placement can alter maxillary sinus anatomy and influence surgical outcomes. This study presents a computational algorithm for automated segmentation and volumetric assessment of the maxillary sinus from cone-beam computed tomography (CBCT) images, offering a reproducible and clinically oriented tool. Methods: Six sinus samples from four patients undergoing pterygoid or zygomatic implant surgery were analyzed. The algorithm was designed to integrate image binarization, surface detection, and iterative reconstruction to delineate sinus boundaries and compute volumes with minimal operator dependence. Results: Postoperative analyses consistently revealed reductions in sinus volume, with relative changes ranging from 1.1% to 24.5%, validated by expert review. Conclusions: These results demonstrate the feasibility of algorithm-driven volumetric assessment as a non-invasive approach to support surgical planning and monitor anatomical changes. Although limited by the small sample size, this pilot study establishes a foundation for further research and highlights the clinical potential of computational methods to enhance precision and safety in zygomatic implantology.