Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study

Abstract

Background Early detection of tooth-related diseases in patients plays a key role in maintaining their dental health and preventing future complications. Since dentists are not overly attentive to tooth-related diseases that may be difficult to judge visually, many patients miss timely treatment. The 5 representative tooth-related diseases, that is, coronal caries or defect, proximal caries, cervical caries or abrasion, periapical radiolucency, and residual root can be detected on panoramic images. In this study, a web service was constructed for the detection of these diseases on panoramic images in real time, which helped shorten the treatment planning time and reduce the probability of misdiagnosis. Objective This study designed a model to assess tooth-related diseases in panoramic images by using artificial intelligence in real time. This model can perform an auxiliary role in the diagnosis of tooth-related diseases by dentists and reduce the treatment planning time spent through telemedicine. Methods For learning the 5 tooth-related diseases, 10,000 panoramic images were modeled: 4206 coronal caries or defects, 4478 proximal caries, 6920 cervical caries or abrasion, 8290 periapical radiolucencies, and 1446 residual roots. To learn the model, the fast region-based convolutional network (Fast R-CNN), residual neural network (ResNet), and inception models were used. Learning about the 5 tooth-related diseases completely did not provide accurate information on the diseases because of indistinct features present in the panoramic pictures. Therefore, 1 detection model was applied to each tooth-related disease, and the models for each of the diseases were integrated to increase accuracy. Results The Fast R-CNN model showed the highest accuracy, with an accuracy of over 90%, in diagnosing the 5 tooth-related diseases. Thus, Fast R-CNN was selected as the final judgment model as it facilitated the real-time diagnosis of dental diseases that are difficult to judge visually from radiographs and images, thereby assisting the dentists in their treatment plans. Conclusions The Fast R-CNN model showed the highest accuracy in the real-time diagnosis of dental diseases and can therefore play an auxiliary role in shortening the treatment planning time after the dentists diagnose the tooth-related disease. In addition, by updating the captured panoramic images of patients on the web service developed in this study, we are looking forward to increasing the accuracy of diagnosing these 5 tooth-related diseases. The dental diagnosis system in this study takes 2 minutes for diagnosing 5 diseases in 1 panoramic image. Therefore, this system plays an effective role in setting a dental treatment schedule.

Веб-Орієнтований Застосунок Ведення Дентальної Хроніки Для Запису І Аналізу Стану Ротової Порожнини

“…The presented developments in this field can be classified according to the following categories such as reference books [1], educational as well, those that remind you of brushing your teeth [2], those that teach you to brush your teeth [3][4][5], questionnaires [6][7][8], those that aimed at managing the dental journal and planning the work process [9,10], diet diaries [11]. There is the latest study that use an artificial intelligence to detect dental diseases using the analysis of panoramic images in real time [12].…”

Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review

“…Thus, several studies are multi-tasks. Some detection studies involve 2 steps: the first time, identification of the ROI (prone to caries) in the form of a bounding box, circle, or rectangular, and the second time, classification (scoring) of this ROI in caries/no caries [39,42,56,58,62,63]. Other detection studies were carried out in one step.…”

STSN-Net: Simultaneous Tooth Segmentation and Numbering Method in Crowded Environments with Deep Learning

“…Nevertheless, panoramic X-ray images of different patients’ teeth exhibit considerable variations, encompassing diverse pathological presentations and treatment indications [ 12 ]. In addition, the background of panoramic X-ray images can be quite complex, incorporating redundant background information, which may lead to incorrect segmentation [ 13 ]. The accurate segmentation and numbering of teeth in panoramic X-ray images become quite challenging because of these issues, thus impeding the practical utility of automated intelligent segmentation and numbering methods in clinical settings.…”