The aim of this study was to investigate radiographic features of osteosclerosis on digital panoramic radiographs. Osteoscleosis was diagnosed with its radiographic features of amorphous non- expansible radiopacity with unknown etiology. In diagosing osteosclerosis, differential diagnosis is needed from periapical cemental dysplasia, osteoma, benign cementoblastoma, and anatomic structures. Fifty-eight osteosclerosis on digital panoramic radiopgraphs from 46 patients with osteoscleosis were selected for this study. All of the osteosclerosis were found in the mandible. Among them, 53(91.4%) occurred on the posterior region. The mean diameter was 9.1㎜. The internal structure was radiopaque in 39(67.2%) and mixed radiolucent and radiopaque in 19(35.3%). There was no specific effect on the surrounding structures. In 16(27.6%), partial or complete radiolucent margin was noted which might be digital image artifact by enhancement.

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.

Artificial intelligence, has been applied in interpreting osteoporosis on dental panoramic radiograph with high accuracy. The purpose of this study was to investigate the sensitive area of convolutional neural network(CNN), one of artificial intelligence, in interpreting osteoporosis on dental panoramic radiograph. Dental panoramic radiographs taken from 1,170 female (49.19 ±21.91 average age, 21 minimum age, and 84 maximum age) were selected for this study. Two oral maxillofacial radiologists agreed upon interpreting osteoporosis by interpreting mandibular inferior cortical changes. The region of interest included upper and lower jaws for training and testing CNN in interpreting osteoporosis. A filter which was set to look for image characteristics moved through the entire panoramic radiography to identify sensitive areas that distinguish normal groups and osteoporosis patients. In interpreting osteoporosis on panoramic radiograph, CNN responded sensitively at the cancellous bone of the upper and lower jaws while oral maxillofacial radiologists interpreted mandibular inferior cortical change.

This study aimed to measure ramal lengths and angles on panoramic radiography applying a polar coordinate system for analyzing facial asymmetry within normal range. Panoramic radiographs taken from 15 males and 15 females (mean age 31.33±3.7 yrs in males and 28.87±2.72 yrs in females) with symmetric-looking faces were selected. The polar coordinate system, length of condylar and ramal height and angles between the ramus tangent and the connecting line of the most inferior point of bilateral orbital rim were measured from panoramic radiographic images. Bilateral differences in the ramal and condylar heights and angles were determined by asymmetric index. The polar coordinate applied for analyzing facial asymmetry uses length and angle measure. The normal range of facial asymmetry was measured using mean and standard deviation of asymmetry index of length and angle measure. A new analysis method using polar coordinate system on panoramic radiograph may provide more accurate analysis for facial asymmetry.

This study aimed to investigate the association between carotid artery calcification (CAC) on panoramic radiograph and intima-media thickeness (IMT) measured on ultrasound. Panoramic radiographs which were taken from dental patients aged 50 years and older who visited for dental treatment were screened for the presence of CAC. The study group was composed of seven patients (four males and three females, average age 74.4±4.2 yrs) with CAC detected on panoramic radiographs, and the control group eleven patients (seven males and four females, average age 64.5±10.1 yrs) without CAC. All the patients underwent carotid ultrasonography to measure carotid IMT. The IMT was compared between the groups by nonparametric analysis of covariance (ANCOVA). The range of IMT of the study group was 1.10~2.0 mm, while that of the control group 0.60~1.10 mm. The mean of IMT was 1.50±0.34 mm in the study group and 0.85±0.14 mm in the control group, and there was statistically significant difference between the two groups (p<.01). In conclusion, CAC detected on panoramic radiograph might have an association with atherosclerosis