Evaluation of Clinical Application Potential based on a Deep Learning Technique with Real-Size Dental Panoramic Radiography: A Preliminary Study

Abstract

With the recent advancement of artificial intelligence (AI), data-based research is being actively conducted in the dental medical field. However, there is a limited amoun of research yet based on algorithms using panoramic radiography. This study was conducted to find the standard AI reading that distinguishes the young from the elderly using panoramic radiographic images, and to confirm the applicability of the method as a means of increasing the reliability of a diagnosis. A total of 117 panoramas in A dental clinic were used. The selected radiographic images were classified into two groups: the old group and the young group. To load the classified images into the suggested and designed multi-layer neural network model (modified DarkNet), they were split into 70 % training data and 30 % testing data using the ‘SplitEachLable()’ Matlab function. To identify the old group, the focal class activation mapping or CAM (the height of the alveolar bone and the major places where other treatment actions took place) area was estimated. To identify the young group, a wide CAM area over the entire area was estimated as a feature. These data could be important quantitative indicators of the health of the alveolar bone and of the overall dental condition. Significant results and features were derived to show the potential of quantitative indicators for dental care. The results of this study confirmed the possibility of estimating the alveolar bone age based on AI.