This study was performed as a part of serial experiments of applying convolutional neural network(CNN) in determining osteoporosis on panoramic radiograph. The purpose of this study was to investigate how sensitively CNN determine osteoporosis on cropped panoramic radiograph. Panoramic radiographs from 1268 female patients(mean age 45.2 ± 21.1 yrs) were selected for this study. For the osteoporosis group, 633(mean age 72.2 ± 8.5 yrs) were selected, while for the normal group 635(mean age 28.3 ± 7.0 yrs). AlexNet was utilized as CNN in this study. A multiple-column CNN was designed with two rectangular regions of interest on the mandible inferior area. An occluding method was used to analyze the sensitive area in determining osteoporosis on AlexNet. Testing of AlexNet showed accuracy of 99% in determining osteoporosis on panoramic radiographs. AlexNet was sensitive at the area of cortical and cancellous bone of the mandible inferior area including adjacent soft tissue.

To investigate the perceptions and attitudes of dental hygienists toward radiation safety management in Korea. A total of 800 dental hygienists were randomly selected for an anonymous survey, and 203 of them participated. The questionnaire items included the following: sex, career period, type of installed radiographic equipment, recognition of the diagnostic reference level (DRL), participation in radiation safety education, and attitudes toward radiation protection for both patients and dental hygienists. The participants were divided into two groups according to their years of experience (< 10 years versus ≥ 10 years). The difference between the groups was investigated according to frequency distribution. Fisher’s exact test or Pearson’s chi-square (χ2) test was used as appropriate. A regression analysis was performed to investigate the impact of wearing a thyroid collar for personnel protection during patient radiation exposure. The types of installed radiographic equipment included panoramic radiography (96.1%), cephalometric radiography (76.9%), intraoral radiography (72.9%), and cone-beam computed tomography (69.5%). Significant differences were observed in the learning pathway for the DRL (Fisher’s exact test, p < 0.05), satisfaction with radiation safety education (Pearson’s χ2 test = 5.3975, Pr = 0.02), and use of personnel radiation monitoring systems (Pearson’s χ2 test = 18.1233, Pr = 0.000) between the groups. Significant differences were also observed in personnel protection using a thyroid collar and patient protection during panoramic radiography (odds ratio = 14.2). Dental hygienists with more than 10 years of experience were more satisfied with radiation safety education and more interested in radiation monitoring. Considering career experience, customized, continuous, and effective radiation safety management education should be provided.

Deep convolutional network is a deep learning approach to optimize image recognition. This study aimed to apply DCNN to the reading of mandibular cortical thinning in digital panoramic radiographs. Digital panoramic radiographs of 1,268 female dental patients (age 45.2 ± 21.1yrs) were used in the reading of the mandibular cortical bone by two maxillofacial radiologists. Among the subjects, 535 normal subject’s panoramic radiographs (age 28.6 ±7.4 yrs) and 533 those of osteoporosis pationts (age 72.1 ± 8.7 yrs) with mandibular cortical thinning were used for training DCNN. In the testing of mandibular cortical thinning, 100 panoramic radiographs of normal subjects (age 26.6 ± 4.5 yrs) and 100 mandibular cortical thinning (age 72.5 ± 7.2 yrs) were used. The sensitive area of DCNN to mandibular cortical thinning was investigated by occluding analysis. The readings of DCNN were compared by two maxillofacial radiologists. DCNN showed 97.5% accuracy, 96% sensitivity, and 99% specificity in reading mandibular cortical thinning. DCNN was sensitively responded on the cancellous and cortical bone of the mandibular inferior area. DCNN was effective in diagnosing mandibular cortical thinning.