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.

잔디는 우리 생활에 없어서는 안 될 매우 중요한 유전자원 중의 하나로 최근 급격한 수요 증가와 더불어 잔디와 관 련된 사업규모가 확대되고 있다. 지금까지 잔디는 전통적인 육종방법을 이용하여 개발되어 왔으며, 전통적 육종 기술로 개발할 수 없는 형질들은 분자생물학적 방법을 이용하여 신품종 잔디를 개발하고 있다. 본 연구에서는 소 비자의 요구에 맞는 잔디 신품종을 개발하기 위하여 분자생물학적기법을 이용하여 개발된 제초제저항성 잔디에 전통적 교배육종 기술을 도입하여 잔디 육종기술의 한계점을 극복하고자 하였다. 제초제저항성 들잔디(JGJ21)의 경우 포복경의 생장 및 증식이 빠르고, 토양활착 능력이 우수할 뿐만 아니라 제초제저항성 기능이 있기 때문에 도 로의 법사면 및 경사지에 적합하다. 그러나 공원 및 정원 등의 조경용으로 사용하기 위해서는 보다 키가 작고 밀도 가 높은 잔디형질이 요구된다. 그러므로 본 연구에서는 JG21의 단점을 보완하기 위해서 금잔디와 교배육종을 수 행함으로써 초고 및 초장이 짧고, 잔디 직립경의 생육밀도가 높은 계통을 선발하였으며, 그 중 가장 우수한 계통은 탐라그린3으로 명명하여 특허출원하였다(국내출원번호 10-2014-0025262). 탐라그린3은 공원 및 정원 등에 사용 하여 잔디 재배시 문제가 되고 있는 예초 및 잡초방제에 따른 노동력을 절감할 수 있으며, 농약의 과다사용으로 인 한 환경오염 문제를 해소할 수 있는 고부가가치 경제작물이 될 것이다.

잔디는 전 세계적으로 재배되고 있는 중요한 단자엽 식물중의 하나이며, 세계 4대 작물 중 하나인 옥수수 다음으로 큰 시장을 보유하고 있다 (Lee. 1996). 잔디는 도로, 하천, 비행장의 토양 침식 방지에서부터 주택, 공원, 정원, 골프 장, 스포츠 경기장 등으로 널리 이용되고 있어 매년 잔디조성 면적이 전 세계적으로 증가되고 있는 추세이다. 이러 한 잔디는 주로 전통적인 육종방법에 의하여 신품종이 개발되어져 왔으나 현대의 다양한 소비자의 요구를 완전히 충족시키지 못하고 있다. 그러므로 최근 유전자조작기술을 이용하여 소비자 요구에 맞는 다양한 신품종 개발이 시도되고 있다. 특히 급격한 기후변화로 인한 작물의 생리장해를 극복하기 위하여 다양한 유전자(내염성, 내건성, 내한성, 녹기연장, 음지내성 등)를 이용한 biotic 및 abiotic 스트레스 저항성을 갖는 기능성 형질전환잔디 개발에 많 은 투자를 하고 있다. 본 연구에서는 환경스트레스에 관여하는 유전자 중 벼 유래의 OsWRKY11유전자를 크리핑 벤트그라스와 들잔디에 도입함으로써 고온 및 건조스트레스에 저항성이 있는 잔디를 개발하고자 하였다. 들잔디 와 크리핑 벤트그라스의 완숙종자로부터 배발생 callus를 유도 및 증식하여 OsWRKY11유전자가 도입된 Agrobacterium에 24시간 감염 하였다. 그 후 공동배양, shoot 유도 및 선발, root 유도 및 선발 과정을 거쳐 형질전환 식물체를 생산하였으며, 확보된 형질전환 식물체는 분자생물학적 검증을 수행하였다.

들잔디(Zoysia japonica Steud.)는 난지형 잔디로 우리나라를 포함한 동아시아 지역에 자생하고 있다. 내한성이 강 해 국내에서는 전국 어디서나 잔디밭으로 쉽게 조성할 수 있으며, 더불어 내건성, 내서성도 강해 일반 정원용으로 사용되는 경우 자연강우만으로도 생육이 가능하다. 또한 내답압성 뿐만 아니라 심근성이어서 묘소, 공원, 골프장, 경사면 녹화 등 여러 가지 목적으로 이용되고 있다. 최근에는 잔디의 이용범위가 확대되면서 한계점이 있는 전통 육종법 대신 분자육종에 의한 신품종 개발이 활발하게 진행되고 있다. 이 분자육종법을 이용하여 신품종 잔디를 개발하기 위해서 먼저 배양이 쉬운 성숙종자 유래의 캘러스를 유도하고, 이 중 재분화 효율이 높은 캘러스 라인만 을 선발하여 Agrobacterium을 이용한 형질전환을 수행하였다. 본 연구에서는 형질전환 효율을 높일 수 있는 몇가 지 요인들을 조사하여 노화지연 GM 들잔디를 제조하였다. 들잔디의 완숙종자에서 유도/증식하여 선발한 재분화 효율이 높은 배발생 캘러스를 노화지연의 표현형적 특징을 나타내는 애기장대 유래의 AT-hook 유전자들 (ATPG3, ATPG4)을 도입한 Agrobacterium (OD600=0.1)에 24시간 동안 감염하고, 3일간 공동배양, 신초유도 및 선발, 뿌리유 도 및 선발 과정을 거쳐 증식하여 각각 약 20개체씩의 형질전환 식물체를 생산하였다. 확보한 ATPG3, ATPG4 유전 자가 도입된 형질전환식물은 순화/증식하여 분자생물학적 특성 분석, 표현형적 특성 분석, 기능분석 등을 수행하 고 있다.

본 연구는 돌연변이원인 EMS(ethyl-methane-sulfonate)를 이용하여 춘란 잎 돌연변이 품종을 개발하고자 수행되었다. 조직배양기술을 이용하여 발아시킨 춘란 근경에 0.2%의 EMS를 처리함으로써 엽록소 결핍 돌연변이 근경을 유도하였다. EMS 처리시 50%이상의 춘란 근경이 갈변되었으며, 갈변된 근경을 근경증식용 고체배지에 배양하였을때 갈변된 근경의 일부 조직으로부터 새로운 측지근경의 생장이 관찰되었다. 이러한 근경조직을 절단하여 1년간 계대배양하던 중 관찰된 색소변이 근경을 식물체로 재분화시킨 결과 중투, 사피, 산반, 단엽종 등 다양한 잎 돌연변이 식물체들로 분화하였다. 이러한 결과들로부터 EMS 처리에 의해 잎 돌연변이 식물체들이 효과적으로 유도되었음을 확인할 수 있었다. 또한 본 연구결과에서 얻어진 춘란잎 돌연변이 식물체를 대량 증식시킬 수 있는 배양체계도 확립되어있다. 그러므로 본 연구에서 개발한 잎 돌연변이 춘란은 향후 산업적으로 이용이 가능할 것으로 생각된다.