Purpose To analyze the quality of mandibular trabecular structure in postmenopausal women using periapical radiographs. Postmenopausal women are subjected to low bone quality; hence, early detection methods are needed. In addition to bone mineral density (BMD), trabecular architecture must be assessed to determine bone quality. The mandible represents bone quality and allows the assessment of trabecular structure from periapical radiographs. Material and Methods Lumbar (BMDL) and femoral BMD (BMDF) examinations were performed using dual-energy X-ray absorptiometry (DXA) in 31 postmenopausal women and divided into normal, osteopenia, and osteoporotic groups. Periapical radiographs were taken at both posterior sites of the mandible. The region of interest was taken 2 mm from the apical root of the first molar. Trabecular parameters consisting of trabecular thickness (Tb.Th) and bone percentage (BA/TA) were measured using BoneJ. Results Both trabecular parameters were significantly correlated with BMDF [BA/TA (r = 0.3796; p < 0.05) and Tb.Th (r = 0.508; p < 0.05)]. BA/TA and Tb.Th were significantly different between the osteoporosis and normal groups (p < 0.05) contrast to osteopenia and normal groups (p > 0.05). Conclusion Changes in mandibular trabeculae structure in postmenopausal women can be assessed using periapical radiographs.
Currently, research for osteoporosis examination using dental radiographic images is increasing rapidly. Many researchers have used various methods from subject data. It indicates that osteoporosis has become a widespread disease that should be studied more deeply. This study proposes a deep Convolutional Neural Network architecture as a texture feature of dental periapical radiograph for osteoporosis detection. The subject of this study is postmenopausal Javanese women aged over 40 and data measurement result of Bone Mineral Density. The proposed model is divided into stages: 1) stage image acquisition and RoI selection, 2) stage feature extraction and classification. Various experiments with the number of convolution layers (3 layers to 6 layers) and various input block sizes and other hyper parameters were used to get the best model. The best model is obtained when the input image size is greater than 100 and less than 150 and a five of convolution layer, as well as other hyper parameters, including epochs=100, dropout=0.5, learning rate=0.0001, batch size= 16 and loss function using Adam's optimization. Validation and testing accuracy achieved by the best model is 98.10%, and 92.50. The research shows that the bigger images provide additional information about trabecular patterns in normal, osteopenia and osteoporosis classes, so that the proposed method using deep convolutional neural network as textural feature of the periapical radiograph achieves a good performance for detection osteoporosis.
Based on interviews with staff nutrition Health Office (Dikes) West Lombok, that is not currently availableinformation systems that can be used to input data monitoring nutritional status of children. So it still takes avery long time to get the right information related to monitoring the nutritional status of children and familiesaware of nutrition per each district. The primary data sourced directly from the community gathered by Puskesmas officers. Analysis of the data needed to meet the needs of data input, process and report to the monitoring system of nutritional status include: site identification, the identity of the household, the habit ofweighing the family members, the question for pregnant or postpartum mothers, the nutritional intake of thefamily, the identity of a toddler, a child's weight. The expected benefits of the outcomes defined as follows:enhance the ability to analyze the situation of food and nutrition in every region, able to set the priority handlingof food and nutrition, able to monitor and evaluate the development of food and nutrition, improve communityhealth status is marked as well as out of the category of problematic areas of health, especially malnutrition andless.
Osteoporosis is a disease that is not only a national issue but has also become a global issue. Although morbidity and mortality rates are relatively low in osteoporosis, fractures because of the disease makes the sufferer feel sick and suffering and affects socio-economic conditions in terms of health care systems and communities. Osteoporosis can be prevented by conducting early detection. Currently, DEXA is used to perform an osteoporosis test that becomes the World Health Organization (WHO) standard. However, the examination with DEXA is still relatively expensive. It technically can’t show the bones’ architecture. So that the examination method using a bone image that has trabeculae like wrist, thigh, jaw, hand, or foot is developed. Some research results on the osteoporosis examination system are presented in this article. The methods include such processes as image acquisition, image enhancement, image analysis (extraction and feature selection), as well as the classification process. Survey results showed that feature extraction, feature selection, and the classification method are selected based on the expected input and output system. Each method has a different level of accuracy
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