Artificial intelligence in diagnosis of maxillary sinusitis: A clinical study

Maxillary sinusitis is a common inflammatory condition often diagnosed through imaging modalities such as "cone-beam computed tomography (CBCT)". A prospective clinical research was conducted involving 200 patients with suspected maxillary sinusitis. The AI model achieved a sensitivity of 89.1%, specificity of 91.7%, positive predictive value of 92.5% and negative predictive value of 87.8% and overall accuracy of 90.4%. The area under the ROC curve was 0.948, indicating excellent diagnostic discrimination. AI demonstrates high reliability in diagnosing maxillary sinusitis on CBCT scans and may serve as an effective adjunct in clinical imaging interpretation, enhancing diagnostic efficiency and consistency.