Deep Learning-Powered Dental Diagnostics: Tooth Localization and Condition Assessment from Bitewing X-Rays

Abstract

Periodontitis is a prevalent dental disease marked by bacterial infection of the alveolar bone surrounding the tooth; early identification and precise intervention are essential to avert severe outcomes, together with tooth loss. Historically, the prognosis of periodontal ailment relies upon at the guide identification and category through dental specialists, requiring substantial skill and often proving to be time-consuming. This study examines the application of sophisticated neural network architectures to automate the detection and categorization of periodontitis using dental imaging datasets. Convolutional Neural Networks (CNNs) were applied to assess dental pictures, permitting early illness detection and decreasing dependence on guide evaluations. A comparative investigation of several optimization strategies in neural networks become accomplished to assess their effect on detection performance. The findings reveal that the proposed method attained a detection accuracy of 96.93%, illustrating the capability of automated structures to improve diagnostic precision, efficiency, and scalability in periodontitis detection. This method could markedly beautify patient effects while optimizing healthcare workflows.