This study aimed to investigate the difference that convolutional neural network(CNN) shows in the determining osteoporosis on panoramic radiograph by performing a paired test by inputting the original image and the limited image including the cortical bone of the posterior border of the mandible used by radiologists. On panoramic radiographs of a total of 661 subjects (mean age 66.3 years ± 11.42), the area including the cortical bone of the posterior part of the mandible was divided into the left and right sides, and the ROI was set, and the remaining area was masked in black to form the limited image. For training of VGG-16, panoramic radiographs of 243 osteoporosis subjects (mean age 72.67 years ± 7.97) and 222 normal subjects (mean age 53.21 years ± 2.46) were used, and testing 1 and testing 2 were performed on the original and limited images, respectively, using panoramic radiographs of 51 osteoporosis subjects (mean age 72.78 years ± 8.3) and 47 normal subjects (mean age 53.32 years ± 2.81). The accuracy of VGG-16 for determining osteoporosis was 97%, in the testing 1 and 100% in the testing 2. When determining osteoporosis on the original image, CNN showed sensitivity in a wide range of areas including not only the inferior cortical bone of the mandible but also the maxillary and mandibular cancellous bone, cervical spine, and zygomatic bone. When the same ROI including the lower inferior cortical border of the mandible of the osteoporosis group was applied and the sensitive region was compared between the original image and the limited image, the original image showed wider sensitive region in cancellous bone and cortical bone than on the limited image (p<.05). Since osteoporosis is a disease that affects throughout the skeletal system, this finding seems very valid.

Previous researches have revealed that dental panoramic radiographs routinely taken in dental clinics can be useful to diagnose low bone density. The purpose of this study is to investigate the prevalence, awareness and treatment rate of low bone density of females utilizing dental panoramic images. Four-hundred-and-fifteen female patients(mean age 70.4 yrs ± 11.4 yrs) between the age of 50s to 90s, at Chonnam National University Dental Hospital were randomly selected for this study. The panoramic radiographs taken from the patients were reviewed for the purpose of interpreting suspected low bone density(SLBD) on the basis of mandibular cortex index. Awareness and treatment rates of osteoporosis were investigated based on electronic records using the past medical history. As a result, the prevalence rate was 42.17%(175 in 415), the osteoporosis-awareness rate 22.3%(39 in 175), and the treatment rate 87%(34 in 39), showing that the osteoporosis-awareness rate was low, but the treatment rate was relatively high. In conclusion, it can be suggested that osteoporosis-awareness rate by diagnosing SLBD with dental panoramic radiographs be increased to help patients to receive proper treatment.

This study aimed to investigate which areas AI is sensitive when inputting panoramic radiographs with dental area masked and when inputting unmasked ones. Therefore, the null hypothesis of this study was that masking dental area would not make a difference in the sensitive areas of osteoporosis determination of AI. For this study 1165 female(average age 48.4 ± 23.9 years) from whom panoramic radiographs were taken were selected. Either osteoporosis or normal should be clearly defined by oral and maxillofacial radiologists. The panoramic radiographs from the female were classified as either osteoporosis or normal according to the mandibular inferior cortex shape. VGG-16 model was used to get training, validating, and testing to determine between osteoporosis or normal. Two experiments were performed; one using unmasked images of panoramic radiographs, and the other using panoramic radiographs with dental region masked. In two experiments, accuracy of VGG-16 was 97.9% with unmasked images and 98.6% with dental-region-masked images. In the osteoporosis group, the sensitive areas identified with unmasked images included cervical vertebrae, maxillary and mandibular cancellous bone, dental area, zygomatic bone, mandibular inferior cortex, and cranial base. The osteoporosis group shows sensitivity on mandibular cancellous bone, cervical vertebrae, and mandibular inferior cortex with masked images. In the normal group, when unmasked images were input, only dental region was sensitive, while with masked images, only mandibular cancellous bone was sensitive. It is suggestive that when dental influence of panoramic radiographs was excluded, AI determined osteoporosis on the mandibular cancellous bone more sensitively.

The purpose of this study was to verify the sensitive areas when the AI determines osteoporosis for the entire area of the panoramic radiograph. Panoramic radiographs of a total of 1,156 female patients(average age of 49.0±24.0 years) were used for this study. The panoramic radiographs were diagnosed as osteoporosis and the normal by Oral and Maxillofacial Radiology specialists. The VGG16 deep learning convolutional neural network(CNN) model was used to determine osteoporosis and the normal from testing 72 osteoporosis(average age of 73.7±8.0 years) and 93 normal(average age of 26.4±5.1 years). VGG16 conducted a gradient-weighted class activation mapping(Grad-CAM) visualization to indicate sensitive areas when determining osteoporosis. The accuracy of CNN in determining osteoporosis was 100%. Heatmap image from 72 panoamic radiographs of osteoporosis revealed that CNN was sensitive to the cervical vertebral in 70.8%(51/72), the cortical bone of the lower mandible in 72.2%(52/72), the cranial base area in 30.6%(22/72), the cancellous bone of the mandible in 33.3%(24/72), the cancellous bone of the maxilla in 20.8%(15/72), the zygoma in 8.3%(6/72), and the dental area in 5.6%(4/72). Consideration: it was found that the cervical vertebral area and the cortical bone of the lower mandible were sensitive areas when CNN determines osteoporosis in the entire area of panoramic radiographs.

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.

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.

Menton (Me) deviation is commonly used for diagnosing facial asymmetry. This study compared angle and distance measurement in determining the severity of Me deviation for facial asymmetry diagnosis. Three-Dimensional Computed Tomographic(3D CT) images of 32 patients (mean age 22.5yrs, SD 3.4yrs; 16 male, 16 female) with facial asymmetry were selected for this study. Angle and distance of Me deviation in each patient were obtained and the severity of Me deviation was determined according to the angle and the distance measurement. The severity of Me deviation by angle and distance measurement was compared and statistical analysis was performed. Eight (25%) showed disagreement in severity of Me deviation between the two measurements. The kappa coefficient on the two measurements was 0.67, showing substantial agreement. It is suggested that both angle and distance measurement be performed in determining severity of Me deivation.

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 was carried out to develop new zoysiagrass (Zoysia japonica Steud.) cultivar ‘Halla Green 2’ (Grant number: No. 118). To develop a zoysiagrass cultivar with dwarfism by using the mutation breeding method, the wild type control "Gosan" plants were irradiated using a 30 Gy gamma ray source in 2010. Dwarf mutants were selected from the mutated grasses in successive generations. Dwarf mutant lines were identified and a new zoysiagrass variety Halla Green 2 was developed. The plant height of Halla Green 2 was 3.4 and 1.8 times lower than that of Gosan and Zenith, respectively. This cultivar has dwarf characteristics such as shorter sheath, shorter leaf blade, shorter flag leaf, and shorter third internode of stolon compared to those of Gosan and Zenith. Additionally, the sheaths and leaf blades color of Gosan, Zenith and Halla Green 2 were all light green, whereas their stolons were purple, yellow-green and yellow green, respectively. Trichomes(hairs) were visible on both adaxial and abaxial surfaces of the Gosan leaves, whereas only on the adaxial side of the Zenith and Halla Green 2 leaves. The Halla Green 2 grass showed distinguishable morphological traits compared to those of wild type Gosan and Zenith.

To develop a dwarf turfgrass (Zoysia japonica) cultivar with artificial mutation-induced breeding method, the wild type control "Gosan" plants were exposed to a 30 Gy gamma ray source in 2010. The mutant lines showing short height were selected from successive generations. One of the resulting dwarf lines obtained was registered under the cultivar name of “Halla Green 1” (2016). The dwarf phenotype of the Halla Green 1 includes a reduction of the height by 4.5-fold, an increase in leaf and third internode lengths by about 6- and 2.3-fold, respectively, compared to the Gosan, and approximately 2.4-, 3.8-, and 1.5-fold relative to the Zenith, respectively. In addition, the Halla Green 1 had a sheath of darker green coloring compared to the light green Gosan and Zenith. The leaf blades of Gosan, Zenith and Halla Green 1 were all light green, whereas their stolons were purple, yellow-green and light purple, respectively. Trichomes presented on both adaxial and abaxial surfaces of the Gosan’s leaves, and only on the adaxial side of the Zenith’s leaves, but none on the Halla Green 1 leaves. The Halla Green 1 exhibited sufficiently distinct morphological traits when compared with the wild type Gosan and Zenith that the dwarf phenotype enhances its commercial viability.

All kinds of crops including foods, feeds and turf grasses are damaged frequently by various environmental stresses such as drought, salt, cold, and high temperature, which cause the loss of agronomic productivity. Plants cannot escape from environmental stresses. Thus, plants were evolving in the direction of overcoming environmental stresses. Some genes such as ARF, AB13, NAC, HSF, WRKY respond to environmental stresses have been reported in plants. The genes play a role in stress responses pathway of plants, the transcription factor in response to environmental stress. Typically OsWRKY76 increased the low temperature resistance, AtWRKY28 been reported to be related to the environmental stress. Zoysiagrass (Zoysia japonica Steud.) is used primarily useful for the garden or the golf course. But WRKY, environmental stress-related gene, is unknown in zoysiagrass. Here, we report the analyzing of WRKY genes and response by cold, dehydration and senescence stresses in zoysiagrass. Three WRKY gene (ZjWRKY3, ZjWRKY5, ZjWRKY7) cloning from zoysiagrass. It was transformed in arabidopsis and zoysiagrass. It will be a function analysis.

With the purpose of improving ginsenoside production in Korean wild ginseng (Panax ginseng Meyer) mutant adventitious root lines, a synthetic gene encoding squalene synthase (PgSS2) was placed under the control of 35S promoter and transferred to Panax ginseng. Embryogenic callus obtained from ginseng adventitious root lines were transformed by infection with A. tumefaciens strain EHA105 containing the PgSS2 gene. Ten phosphinothricin-resistant plants were generated on selection medium, and the transgene integration and expression in these plants were confirmed by PAT test strip, RT-PCR and Southern hybridization. Ginsenoside analysis by HPLC revealed that the total contents of the 8 ginsenoside types (Rg1, Re, Rf, Rh1, Rb1, Rc, Rb2, Rd) in transgenic adventitious root lines were about 1.6-fold higher than that of the mutant control line (MCL1). This transformation method may facilitate the improvement of Panax ginseng in terms of the accumulation levels of ginsenoside.