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    Artificial intelligence-based fully automatic 3D paranasal sinus segmentation
    (Oxford Univ Press, 2026) Yigit, Meryem Kaygisiz; Pinarbasi, Alp; Etoz, Meryem; Duman, Suayip Burak; Bayrakdar, Ibrahim Sevki
    Objectives Precise 3D segmentation of paranasal sinuses is essential for accurate diagnosis and treatment. This study aimed to develop a fully automated segmentation algorithm for the paranasal sinuses using the nnU-Net v2 architecture.Methods The nnU-Net v2-based segmentation algorithm was developed using Python 3.6.1 and the PyTorch library, and its performance was evaluated on a dataset of 97 cone beam CT (CBCT) scans. Ground truth annotations were manually generated by expert radiologists using the 3D Slicer software, employing a polygonal labelling technique across sagittal, coronal, and axial planes. Model performance was assessed using several quantitative metrics, including accuracy, Dice coefficient (DC), sensitivity, precision, Jaccard index, area under the curve (AUC), and 95% Hausdorff distance (95% HD).Results The nnU-Net v2-based algorithm demonstrated high segmentation performance across all paranasal sinuses. DC values were 0.94 for the frontal, 0.95 for the sphenoid, 0.97 for the maxillary, and 0.88 for the ethmoid sinuses. Accuracy scores exceeded 99% for all sinuses. The 95% HD values were 0.51 mm for both the frontal and maxillary sinuses, 0.85 mm for the sphenoid sinus, and 1.17 mm for the ethmoid sinus. Jaccard indices were 0.90, 0.91, 0.94, and 0.80, respectively.Conclusions This study highlights the high accuracy and precision of the nnU-Net v2-based CNN model in the fully automated segmentation of paranasal sinuses from CBCT images. The results suggest that the proposed model can significantly contribute to clinical decision-making processes, facilitating diagnostic and therapeutic procedures.
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    Evaluation of the maxillary sinus volume and dimensions in different skeletal classes using cone beam computed tomography
    (2021) Asantogrol, Firdevs; Etoz, Meryem; Topsakal, Kubra Gulnur; Can, Fatma Ezgi
    Aim: The extension of the maxillary sinus is an important issue for fixed orthodontic treatments and maxillofacial surgery. The aim of this study is to investigate the dimensions and volume of the maxillary sinus in different skeletal classes and, also the effect on the anteroposterior growth pattern of the maxilla.Materials and Methods: The cone-beam computed tomography (CBCT) images of 48 patients were obtained from the archive of the Department of Oral and Maxillofacial Radiology. The CBCT images were taken prior to orthognathic surgery for the surgical planning of all patients. According to the sagittal skeletal position of the maxilla, the patients were divided into three groups: normal maxilla group, retrognathic maxilla group and, prognathic maxilla group. Dimensional and volumetric measurements of the maxillary sinus were performed by the same oral and maxillofacial radiologist.Results: Although no statistical difference was observed between different skeletal groups regarding the maxillary sinus dimensions and volume, the results did show that there was an inverse and statistically significant correlation between the left maxillary sinus width and age (p0.05). There was a statistically significant difference between males and females for the width, height, and depth of right maxillary sinus, the right maxillary sinus volume, the height and depth of left maxillary sinus.Conclusion: In conclusion, for orthodontists and maxillofacial surgeons, the dimensional and volumetric measurements performed by CBCT act as a pathfinder role in the insertion of miniscrews, orthodontic tooth movement through the maxillary sinus, and the orthognathic surgeries such as Le Fort osteotomies.