We developed an automatic method for staging periodontitis on dental panoramic radiographs using the deep learning hybrid method. A novel hybrid framework was proposed to automatically detect and classify the periodontal bone loss of each individual tooth. The framework is a hybrid of deep learning architecture for detection and conventional CAD processing for classification. Deep learning was used to detect the radiographic bone level (or the CEJ level) as a simple structure for the whole jaw on panoramic radiographs. Next, the percentage rate analysis of the radiographic bone loss combined the tooth long-axis with the periodontal bone and CEJ levels. Using the percentage rate, we could automatically classify the periodontal bone loss. This classification was used for periodontitis staging according to the new criteria proposed at the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The Pearson correlation coefficient of the automatic method with the diagnoses by radiologists was 0.73 overall for the whole jaw (p < 0.01), and the intraclass correlation value 0.91 overall for the whole jaw (p < 0.01). The novel hybrid framework that combined deep learning architecture and the conventional CAD approach demonstrated high accuracy and excellent reliability in the automatic diagnosis of periodontal bone loss and staging of periodontitis.
Molar-incisor malformation (MIM) is a recently defined dental abnormality of molar root and incisors, and introduced as one of the causes of periapical and periodontal abscess. The purpose of this study was to investigate the clinical and radiological features of MIM, with special emphasis on various medical history. A total of 38 patients with MIM were included in this study. Radiographic features and clinical data, including medical history, chief complaint, associated complications, treatment, and prognosis, were retrospectively investigated. On radiographs, the affected molars showed short, slender, underdeveloped roots and constricted pulp chambers. All affected incisors and canines exhibited dilacerated short roots, wedge-shaped defect on the cervical part of the crown. Complications included periodontal bone loss (52.6%), endodontic lesion (50.0%), and endodontic-periodontal lesion (28.9%). The medical histories of the patients with MIM indicate that almost all (94.7%) were hospitalized due to problems during the neonatal period. MIM may cause various dental problems, such as periapical and periodontal abscess and early loss of the affected teeth. The early diagnosis of MIM on radiographs and appropriate treatment will contribute to a favorable prognosis, especially for young and adolescent patients.
Artificial intelligence, which has been actively applied in a broad range of industries in recent years, is an active area of interest for many researchers. Dentistry is no exception to this trend, and the applications of artificial intelligence are particularly promising in the field of oral and maxillofacial (OMF) radiology. Recent researches on artificial intelligence in OMF radiology have mainly used convolutional neural networks, which can perform image classification, detection, segmentation, registration, generation, and refinement. Artificial intelligence systems in this field have been developed for the purposes of radiographic diagnosis, image analysis, forensic dentistry, and image quality improvement. Tremendous amounts of data are needed to achieve good results, and involvement of OMF radiologist is essential for making accurate and consistent data sets, which is a time-consuming task. In order to widely use artificial intelligence in actual clinical practice in the future, there are lots of problems to be solved, such as building up a huge amount of fine-labeled open data set, understanding of the judgment criteria of artificial intelligence, and DICOM hacking threats using artificial intelligence. If solutions to these problems are presented with the development of artificial intelligence, artificial intelligence will develop further in the future and is expected to play an important role in the development of automatic diagnosis systems, the establishment of treatment plans, and the fabrication of treatment tools. OMF radiologists, as professionals who thoroughly understand the characteristics of radiographic images, will play a very important role in the development of artificial intelligence applications in this field.
The results, based on a larger sample size compared with previous studies, can be applied as guidelines for the diagnosis of tonsilloliths on CT images.
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