Abstract :
Due to their varied presentations and possible side effects, maxillary sinus pathologies—which include diseases including sinusitis, polyps, cysts, and neoplasms—present substantial clinical problems. For these diseases to be effectively treated and to avoid long-term morbidity, early and accurate detection is essential. Cone Beam Computed Tomography (CBCT), which provides high-resolution, three-dimensional imaging that enables superior visibility of the sinus structures, has become a useful tool in the diagnosis and assessment of maxillary sinus disorders. To help with the interpretation and diagnosis of diseases, however, the growing amount of CBCT data demands the use of cutting-edge technologies. In medical imaging, artificial intelligence (AI), especially through machine learning and deep learning methods, is revolutionizing the field. AI can greatly improve the diagnosis of small sinus abnormalities by automating and improving the processing of CBCT pictures, frequently outperforming conventional diagnostic techniques. The integration of AI into CBCT-based diagnosis of maxillary sinus diseases is examined in this communication, with a focus on how it can enhance early detection, accuracy, and efficiency. The aim is to highlights how AI is transforming clinical practice by helping radiologists diagnose patients more accurately and quickly.
Keyword :
Maxillary sinus pathologies, Cone beam computed tomography (CBCT), Artificial intelligence (AI), Medical imaging, Pathology detection.