• Journal of Korean Academy of Nursing
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• v.36 no.2
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• pp.286-298
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• 2006

Purpose: This study was to develop an online 'alternative therapy and health promotion' class for university students and to evaluate its changes. Method: The online class was developed based on the Instructional Systems Development(ISD) model and model of Web-Based Instruction(WBI) developmental process. This was a quasi-experimental, one group pretest-posttest design. The subjects of this study were 130 students in 3 universities, and they were provided the cyber class for 16 weeks. Data was analyzed by descriptive and plural answer statistics, and paired t-test. Results: The cyber class was developed in five steps : analysis, design, data collection and reconstruction, programing and publishing, and evaluation. The results of program evaluation were positive, which included learning 3.47. system 3.57, and learning satisfaction 3.64 on the scale of 5. The posttest scores of cognition and reliability of alternative therapy were higher than pretest scores. The posttest score of health promoting lifestyle(t=-5.051, p=.000) and perceived health status(t=2.979, p=.003) were significantly higher than those of the pretest. Conclusion: These results suggest that the cyber class is a positive method in increasing a cognition, reliability of alternative therapy, and is effective to improve a health promotion lifestyle and perceived health status for the university students.

• Imaging Science in Dentistry
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• v.51 no.3
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• pp.299-306
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• 2021

Purpose: This study aimed to propose a fully automatic landmark identification model based on a deep learning algorithm using real clinical data and to verify its accuracy considering inter-examiner variability. Materials and Methods: In total, 950 lateral cephalometric images from Yonsei Dental Hospital were used. Two calibrated examiners manually identified the 13 most important landmarks to set as references. The proposed deep learning model has a 2-step structure-a region of interest machine and a detection machine-each consisting of 8 convolution layers, 5 pooling layers, and 2 fully connected layers. The distance errors of detection between 2 examiners were used as a clinically acceptable range for performance evaluation. Results: The 13 landmarks were automatically detected using the proposed model. Inter-examiner agreement for all landmarks indicated excellent reliability based on the 95% confidence interval. The average clinically acceptable range for all 13 landmarks was 1.24 mm. The mean radial error between the reference values assigned by 1 expert and the proposed model was 1.84 mm, exhibiting a successful detection rate of 36.1%. The A-point, the incisal tip of the maxillary and mandibular incisors, and ANS showed lower mean radial error than the calibrated expert variability. Conclusion: This experiment demonstrated that the proposed deep learning model can perform fully automatic identification of cephalometric landmarks and achieve better results than examiners for some landmarks. It is meaningful to consider between-examiner variability for clinical applicability when evaluating the performance of deep learning methods in cephalometric landmark identification.

• Imaging Science in Dentistry
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• v.49 no.1
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• pp.19-26
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• 2019

Purpose: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. Materials and Methods: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. Results: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. Conclusion: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

• Imaging Science in Dentistry
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• v.43 no.3
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• Imaging Science in Dentistry
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• v.50 no.2
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• pp.169-174
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• 2020

Purpose: Periodontal disease causes tooth loss and is associated with cardiovascular diseases, diabetes, and rheumatoid arthritis. The present study proposes using a deep learning-based object detection method to identify periodontally compromised teeth on digital panoramic radiographs. A faster regional convolutional neural network (faster R-CNN) which is a state-of-the-art deep detection network, was adapted from the natural image domain using a small annotated clinical data- set. Materials and Methods: In total, 100 digital panoramic radiographs of periodontally compromised patients were retrospectively collected from our hospital's information system and augmented. The periodontally compromised teeth found in each image were annotated by experts in periodontology to obtain the ground truth. The Keras library, which is written in Python, was used to train and test the model on a single NVidia 1080Ti GPU. The faster R-CNN model used a pretrained ResNet architecture. Results: The average precision rate of 0.81 demonstrated that there was a significant region of overlap between the predicted regions and the ground truth. The average recall rate of 0.80 showed that the periodontally compromised teeth regions generated by the detection method excluded healthiest teeth areas. In addition, the model achieved a sensitivity of 0.84, a specificity of 0.88 and an F-measure of 0.81. Conclusion: The faster R-CNN trained on a limited amount of labeled imaging data performed satisfactorily in detecting periodontally compromised teeth. The application of a faster R-CNN to assist in the detection of periodontally compromised teeth may reduce diagnostic effort by saving assessment time and allowing automated screening documentation.

• Korean Journal of Acupuncture
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• v.39 no.3
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• pp.71-83
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• 2022

Objectives : Instructional design is the systematic approach to the Analysis, Design, Development, Implementation, and Evaluation of learning materials and activities. We aimed to apply the rapid prototyping to instructional systems design (RPISD) in meridianology laboratory, a subject in which students train acupuncture to develop lesson plan. Methods : The needs of the stakeholders including client, subject matter expert and students were analyzed using the performance needs analysis model. Task analysis was implemented by observation and interview. First prototype was drafted and implemented in meridianology laboratory class once. The second prototype was modified from the first, by usability evaluation of the stakeholders. Results : The client requested an electronically documented manual to improve the quality of acupuncture training. The learner requested an extension of practice time and detailed practice guidelines. The main problems of students' performance were some cases of violation of clean needle technique, the lack of communication between the operator and recipient in direct, and lack of confidence in their own performance. Stakeholders were generally satisfied with the proposed first prototype. Second prototype of lesson plan was produced by modifying some contents. Conclusions : A lesson plan was developed by applying the systematic RPISD model. It is expected that the developed instructional design may contribute to the quality improvement of meridianology laboratory education.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.393-398
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• 2022

Purpose: This study proposed a generative adversarial network (GAN) model for T2-weighted image (WI) synthesis from proton density (PD)-WI in a temporomandibular joint(TMJ) magnetic resonance imaging (MRI) protocol. Materials and Methods: From January to November 2019, MRI scans for TMJ were reviewed and 308 imaging sets were collected. For training, 277 pairs of PD- and T2-WI sagittal TMJ images were used. Transfer learning of the pix2pix GAN model was utilized to generate T2-WI from PD-WI. Model performance was evaluated with the structural similarity index map (SSIM) and peak signal-to-noise ratio (PSNR) indices for 31 predicted T2-WI (pT2). The disc position was clinically diagnosed as anterior disc displacement with or without reduction, and joint effusion as present or absent. The true T2-WI-based diagnosis was regarded as the gold standard, to which pT2-based diagnoses were compared using Cohen's ĸ coefficient. Results: The mean SSIM and PSNR values were 0.4781(±0.0522) and 21.30(±1.51) dB, respectively. The pT2 protocol showed almost perfect agreement(ĸ=0.81) with the gold standard for disc position. The number of discordant cases was higher for normal disc position (17%) than for anterior displacement with reduction (2%) or without reduction (10%). The effusion diagnosis also showed almost perfect agreement(ĸ=0.88), with higher concordance for the presence (85%) than for the absence (77%) of effusion. Conclusion: The application of pT2 images for a TMJ MRI protocol useful for diagnosis, although the image quality of pT2 was not fully satisfactory. Further research is expected to enhance pT2 quality.

• Korean Parent-Child Health Journal
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• v.16 no.1
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• pp.27-36
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• 2013

Purpose: The purpose of this study was to develop a nocturnal emission and menstruation education program using CAI for Korean elementary school children. Methods: The research design was methodological study a ISD model 4 fields into sub contents in each field of the nocturnal emission and menstruation by CD titles. And to verify the effect of the education program, performance evaluation of the educational program for structured questionnaire was conducted on 120 late school-age children on June, 2010. Results: The results of this study were as follows; Through this, developed program was proved its effectiveness in enhancing knowledge level on nocturnal emission and menstruation in late school-age children(t=14.03, p<.001, t=11.52, p<.001). Conclusion : Accordingly, this program is expected to be an educational program to be used in various educational institutes, communities, and home as well as self-study that allows children themselves to study repeatedly, choosing the contents they want, whenever they hope as an educational program on nocturnal emission and menstruation in school-age children. In addition, it is suggested that various fields of programs should develop in consideration of early sexual maturity.

• Imaging Science in Dentistry
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• v.50 no.3
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• pp.183-192
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• 2020

Purpose: This study was performed to assess the accuracy of preoperative cone-beam computed tomography (CBCT), when justified for other reasons, in locating the apical foramen and establishing the working length. Materials and Methods: Six electronic databases were searched for studies on this subject. All studies, of any type, were included if they compared measurements of working length with preoperative CBCT to measurements using an electronic apex locator (EAL) or histological reference standard. Due to the high levels of heterogeneity, an inverse-variance random-effects model was chosen, and weighted mean differences were obtained with 95% confidence intervals and P values. Results: Nine studies were included. Compared to a histological reference standard, CBCT indicated that the apical foramen was on average 0.40 mm coronal of its histological position, with a mean absolute difference of 0.48 mm. Comparisons were also performed to an EAL reference standard, but the conclusions could not be considered robust due to high levels of heterogeneity in the results. Conclusion: A low level of evidence is produced suggesting that preoperative CBCT shows the apical foramen to be on average 0.40 mm coronal to its histological position, with a mean absolute difference of 0.48 mm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5284-5291
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• 2012

This research is to develop the Web-Based Ventilator Management Education Program that reflects the needs of nursing site nursing which is intended to help nursing duties. ISD model-building process has been developed for the analysis, design, development, operation, and evaluation of methods. The education program was developed from April to July 2011, and SPSS 18.0 was used for data analysis. The analysis stage, document review and requirement analysis, content analysis, learner analysis, technology and environment analysis have been executed. Through the processes, higher education requirements for the practical ventilator, a large number of subjects under the age of 30, and the career of less than 3 years of ICU were searched. At the design stage, the education content that was presented by the content expert group was executed in information design, mutual interaction design, synchronization design through discussion with program experts. At the development stage, author made the story flow and gathered data and integrated it through the review of related document and data. At the operation and evaluation stage, author executed the developed program and revised and supplemented it on the basis of the evaluation results through the experts and subjects evaluation. The Web-Based Ventilator Management Education Program could contribute to the improvement of nursing because the program has been developed to reflect the diverse needs of nursing practice in the process of building program.