Automated Classification of Maxillary Sinus Ostium Patency Using a ConvNeXt-Tiny

Abstract

Background/Objectives: The patency and anatomical location of the maxillary sinus ostium are critical for preventing postoperative complications in dental implant planning and sinus lift surgeries in the posterior maxilla. Narrowing or obstruction of the ostium carries risks, including the development of acute/chronic sinusitis and bone graft failure after surgery. These risks must be carefully evaluated using preoperative radiographic images. It is time-consuming for physicians to manually perform this process, and details are overlooked due to a lack of clinical experience, which can increase surgical risks. Methods: This study aims to overcome these clinical challenges and improve the reliability of radiographic evaluation. In this study, a hybrid deep learning model is proposed for the automatic detection of the maxillary sinus ostium. The proposed model combines the local feature extraction power of CNN-based models with the global context modeling capabilities of transformer-based models, creating an effective model. Additionally, the gated fusion technique efficiently combines features from various designs, significantly enhancing classification performance. Results: The proposed model was compared with six different ViT and CNN architectures established in the literature. While the highest test accuracy among pre-trained models was 89.36%, the proposed hybrid model achieved 95.03%, demonstrating strong clinical diagnostic performance. Conclusions: Based on the performance metrics obtained, we believe the proposed model can be used to determine the patency of the maxillary sinus ostium. This will lighten the workload for specialists and minimize traditional errors.