Objectives To evaluate the diagnostic performance of a deep convolutional neural network (DCNN)-based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists.  Methods: Oral and maxillofacial radiologists with >10 years of experience reviewed the panoramic radiographs of 1268 females {mean [± standard deviation (SD)] age: 52.5 ± 22.3 years} and made a diagnosis of osteoporosis when cortical erosion of the mandibular inferior cortex was observed. Among the females, 635 had no osteoporosis [mean (± SD) age: 32.8 ± SD 12.1 years] and 633 had osteoporosis (72.2 ± 8.5 years). All panoramic radiographs were analysed using three CAD systems, single-column DCNN (SC-DCNN), single-column with data augmentation DCNN (SC-DCNN Augment) and multicolumn DCNN (MC-DCNN). Among the radiographs, 200 panoramic radiographs [mean (± SD) patient age: 63.9 ± 10.7 years] were used for testing the performance of the DCNN in detecting osteoporosis in this study. The diagnostic performance of the DCNN-based CAD system was assessed by receiver operating characteristic (ROC) analysis.  Results: The area under the curve (AUC) values obtained using SC-DCNN, SC-DCNN (Augment) and MC-DCNN were 0.9763, 0.9991 and 0.9987, respectively.  Conclusions: The DCNN-based CAD system showed high agreement with experienced oral and maxillofacial radiologists in detecting osteoporosis. A DCNN-based CAD system could provide information to dentists for the early detection of osteoporosis, and asymptomatic patients with osteoporosis can then be referred to the appropriate medical professionals.

Thickness and morphological changes of mandibular inferior cortical bone are associated with BMD, independent of age, height and weight. These results suggest that MI, MCI and SVE may be useful indices for the diagnosis of osteoporosis in a Korean population.

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Pyrrolidine dithiocarbamate (PDTC), an antioxidant with a metal-chelating activity, has been used widely to inhibit the expression of inflammatory genes in vitro and in vivo. This study investigated whether PDTC has an antimicrobial activity against various bacteria. The antibacterial activity of PDTC and other compounds was evaluated in vitro by the broth microdilution method against Porphyromonas gingivalis, Actinobacillus actinomycetemcomitans, Staphylococcus aureus, and Escherichia coli. Bacterial growth was inhibited by PDTC, where a wide range of sensitivity was demonstrated among the tested bacteria. The antibacterial activity of PDTC was reduced by the addition of copper chloride; in contrast, it was enhanced considerably by zinc chloride. Two different zinc chelators, Ca-saturated EDTA (Ca-EDTA) and N,N,N',N'-tetrakis (2-pyridylmethyl) ethylenediamine, blocked the antibacterial activity of PDTC, whereas Zn-EDTA failed to reduce the activity of PDTC. These results demonstrate for the first time that PDTC possesses an antibacterial activity, for which zinc is required, and suggest that PDTC, possessing a dual anti-inflammatory and antibacterial activity, may be considered for topical use for inflammatory diseases of bacterial origin.

Dental panoramic radiography (DPR) is a method commonly used in dentistry for patient diagnosis. This study presents a new technique that combines a regional convolutional neural network (RCNN), Single Shot Multibox Detector, and heuristic methods to detect and number the teeth and implants with only fixtures in a DPR image. This technology is highly significant in providing statistical information and personal identification based on DPR and separating the images of individual teeth, which serve as basic data for various DPR-based AI algorithms. As a result, the mAP(@IOU = 0.5) of the tooth, implant fixture, and crown detection using the RCNN algorithm were obtained at rates of 96.7%, 45.1%, and 60.9%, respectively. Further, the sensitivity, specificity, and accuracy of the tooth numbering algorithm using a convolutional neural network and heuristics were 84.2%, 75.5%, and 84.5%, respectively. Techniques to analyze DPR images, including implants and bridges, were developed, enabling the possibility of applying AI to orthodontic or implant DPR images of patients.