Missing Teeth and Restoration Detection Using Dental Panoramic Radiography Based on Transfer Learning With CNNs

• Main Authors: Chen, Shih-Lun, Chen, Tsung-Yi, Huang, Yen-Cheng, Chen, Chiung-An, Chou, He-Sheng, Huang, Ya-Yun, Lin, Wei-Chi, Li, Tzu-Chien, Yuan, Jia-Jun, Abu, Patricia Angela R
• Format: text
• Published: Archīum Ateneo 2022
• Subjects: Biomedical image; panoramic image; histogram equalization; flat-field correction; tooth segmentation; tooth position; CNN; transfer learning; Alexnet; GoogLeNet; Squeezenet; Computer Sciences; Dentistry; Medicine and Health Sciences; Physical Sciences and Mathematics
• Online Access: https://archium.ateneo.edu/discs-faculty-pubs/351; https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1351&context=discs-faculty-pubs
• Institution: Ateneo De Manila University

Common dental diseases include caries, periodontitis, missing teeth and restorations. Dentists still use manual methods to judge and label lesions which is very time-consuming and highly repetitive. This research proposal uses artificial intelligence combined with image judgment technology for an improved efficiency on the process. In terms of cropping technology in images, the proposed study uses histogram equalization combined with flat-field correction for pixel value assignment. The details of the bone structure improves the resolution of the high-noise coverage. Thus, using the polynomial function connects all the interstitial strands by the strips to form a smooth curve. The curve solves the problem where the original cropping technology could not recognize a single tooth in some images. The accuracy has been improved by around 4% through the proposed cropping technique. For the convolutional neural network (CNN) technology, the lesion area analysis model is trained to judge the restoration and missing teeth of the clinical panorama (PANO) to achieve the purpose of developing an automatic diagnosis as a precision medical technology. In the current 3 commonly used neural networks namely AlexNet, GoogLeNet, and SqueezeNet, the experimental results show that the accuracy of the proposed GoogLeNet model for restoration and SqueezeNet model for missing teeth reached 97.10% and 99.90%, respectively. This research has passed the Research Institution Review Board (IRB) with application number 202002030B0.