The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis.
Using automated information systems for differential diagnosis will improve the efficiency of …

The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis.
Using automated information systems for differential diagnosis will improve the efficiency of …

The proposed methodology aims to revolutionize the diagnosis of Chronic Odontogenic
Rhinosinusitis (CORS) by leveraging advanced artificial intelligence (AI) techniques, with a …

The aim of this study is to develop a new approach for Computed Tomography (CT) image
segmentation based on Convolutional Neural Network (CNN) for detection of Odontogenic …

Accurate paranasal sinus segmentation is essential for reducing surgical complications
through surgical guidance systems. This study introduces a multiclass Convolutional Neural …

Materials and methods We developed Convolutional Neural Networks (CNNs) trained to
detect and classify chronic rhinosinusitis. The database used comprised CT scans of …

Purpose This study developed a convolutional neural network (CNN) model to diagnose
maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic …

Objectives This study, which uses artificial intelligence-based methods, aims to determine
the limits of pathologic conditions and infections related to the maxillary sinus in cone beam …

Computed tomography (CT) is undoubtedly the most reliable and the only method for
accurate diagnosis of sinusitis, while X-ray has long been used as the first imaging …

The detection of maxillary sinus wall is important in dental fields such as implant surgery,
tooth extraction, and odontogenic disease diagnosis. The accurate segmentation of the …