• Journal of The Korean Association For Science Education
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• v.29 no.1
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• pp.106-115
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• 2009

In this study, classroom instructions of teachers who participate in the science teachers' community were videotaped and analyzed to understand their teaching professionalism. Among the "Standards for teaching evaluation of science instruction" developed by Korea Education Curriculum and Assessment, 12 evaluation elements were selected and used for data analysis. First of all, the results indicate that most of the teachers show the highest teaching level in the interaction between student and teacher, and the lowest in the statement of teaching object as the teaching evaluation element. Second, from the viewpoint of the teaching level, all of the teachers at the superior level were veterans whose teaching careers have spanned longer than 15 years. It was found that they used various teaching materials in class and designed meaningful learning programs for their students. Compared with teachers at the superior level, beginning teachers used limited teaching materials due to their lack of experience. In addition, their instruction falls short of flexible management in teaching. The results show that they tend to teach in a somewhat rigid style that does not have sufficient positive interaction with students.

• PNF and Movement
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• v.22 no.1
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• pp.43-54
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• 2024

Purpose: Falls among persons older than 65 years are a significant concern due to their frequency and severity. This study aimed to develop a vest-type embedded artificial intelligence (AI) system capable of detecting and predicting falls in various scenarios. Methods: In this study, we established and developed a vest-type embedded AI system to judge and predict falls in various directions and situations. To train the AI, we collected data using acceleration and gyroscope values from a six-axis sensor attached to the seventh cervical and the second sacral vertebrae of the user, considering accurate motion analysis of the human body. The model was constructed using a neural network-based AI prediction algorithm to anticipate the direction of falls using the collected pedestrian data. Results: We focused on developing a lightweight and efficient fall prediction model for integration into an embedded AI algorithm system, ensuring real-time network optimization. Our results showed that the accuracy of fall occurrence and direction prediction using the trained fall prediction model was 89.0% and 78.8%, respectively. Furthermore, the fall occurrence and direction prediction accuracy of the model quantized for embedded porting was 87.0 % and 75.5 %, respectively. Conclusion: The developed fall detection and prediction system, designed as a vest-type with an embedded AI algorithm, offers the potential to provide real-time feedback to pedestrians in clinical settings and proactively prepare for accidents.

• Information Systems Review
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• v.22 no.1
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• pp.113-124
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• 2020

In the operation of farms, it is very important to evaluate the quality of harvested crops and to classify defective products. However, farmers have difficulty coping with the cost and time required for quality assessment due to insufficient capital and manpower. This study thus aims to detect defects by analyzing the epidermis of fruit using deep learning algorithm. We developed a model that can analyze the epidermis by applying YOLOv3 algorithm based on Region Convolutional Neural Network to video images of peach. A total of four classes were selected and trained. Through 97,600 epochs, a high performance detection model was obtained. The crop failure detection model proposed in this study can be used to automate the process of data collection, quality evaluation through analyzed data, and defect detection. In particular, we have developed an analytical model for peach, which is the most vulnerable to external wounds among crops, so it is expected to be applicable to other crops in farming.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• Korean Journal of Radiology
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• v.25 no.1
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• pp.24-32
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• 2024

Despite improvements in operative techniques and perioperative care, post-hepatectomy liver failure (PHLF) remains the most serious cause of morbidity and mortality after surgery, and several risk factors have been identified to predict PHLF. Although volumetric assessment using imaging contributes to surgical simulation by estimating the function of future liver remnants in predicting PHLF, liver function is assumed to be homogeneous throughout the liver. The combination of volumetric and functional analyses may be more useful for an accurate evaluation of liver function and prediction of PHLF than only volumetric analysis. Gadoxetic acid is a hepatocyte-specific magnetic resonance (MR) contrast agent that is taken up by hepatocytes via the OATP1 transporter after intravenous administration. Gadoxetic acid-enhanced MR imaging (MRI) offers information regarding both global and regional functions, leading to a more precise evaluation even in cases with heterogeneous liver function. Various indices, including signal intensity-based methods and MR relaxometry, have been proposed for the estimation of liver function and prediction of PHLF using gadoxetic acid-enhanced MRI. Recent developments in MR techniques, including high-resolution hepatobiliary phase images using deep learning image reconstruction and whole-liver T1 map acquisition, have enabled a more detailed and accurate estimation of liver function in gadoxetic acid-enhanced MRI.

• Dementia and Neurocognitive Disorders
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• v.23 no.1
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• pp.1-10
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• 2024

Background and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer's disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer's disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions: Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

• The korean journal of orthodontics
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• v.54 no.4
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• pp.229-238
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• 2024

Objective: The study aimed to assess the prevalence of dental malocclusion, orthodontic parameters, and parafunctional habits in children with developmental dyslexia (DD). Methods: Forty pediatric patients (67.5% boys and 32.5% girls, mean age: 11.02 ± 2.53 years, range: 6-15 years) with DD were compared with 40 age- and sex-matched healthy participants for prevalence of dental malocclusion, orthodontic parameters, and parafunctional habits. Dental examinations were performed by an orthodontist. Results: Pediatric patients with DD exhibited a significantly higher prevalence of Angle Class III malocclusion (22.5% vs. 5.0%, P = 0.024), deep bite (27.5% vs. 7.5%, P = 0.019), midline deviation (55.0% vs. 7.5%, P < 0.0001), midline diastemas (32.5% vs. 7.5%, P = 0.010), wear facets (92.5% vs. 15.0%, P < 0.0001), self-reported nocturnal teeth grinding (82.5% vs. 7.5%, P < 0.0001), nail biting (35.0% vs. 0.0%, P < 0.0001), and atypical swallowing (85.0% vs. 17.5%, P < 0.0001) compared to that in healthy controls. Conclusions: Pediatric patients with DD showed a higher prevalence of Class III malocclusion, greater orthodontic vertical and transverse discrepancies, and incidence of parafunctional activities. Clinicians and dentists should be aware of the vulnerability of children with dyslexia for exhibiting malocclusion and encourage early assessment and multidisciplinary intervention.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.77-89
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• 2024

Knee osteoarthritis (OA) is a very common musculoskeletal disorder worldwide. The assessment of knee osteoarthritis, which requires a rapid and accurate initial diagnosis, is determined to be different depending on the currently dispersed classification system, and each classification system has different criteria. Also, because the medical staff directly sees and reads the X-ray pictures, it depends on the subjective opinion of the medical staff, and it takes time to establish an accurate diagnosis and a clear treatment plan. Therefore, in this study, we designed the stenosis length measurement algorithm and Osteophyte detection and length measurement algorithm, which are the criteria for determining the knee osteoarthritis grade, separately using CNN, which is a deep learning technique. In addition, we would like to create a grading system that integrates and complements the existing classification system and show results that match the judgments of actual medical staff. Based on publicly available OAI (Osteoarthritis Initiative) data, a total of 9,786 knee osteoarthritis data were used in this study, eventually achieving an Accuracy of 69.8% and an F1 score of 76.65%.

• Journal of Nutrition and Health
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• v.53 no.3
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• pp.319-328
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• 2020

Purpose: This study was conducted to evaluate the validity of the Gene-Health application in terms of estimating energy and macronutrients. Methods: The subjects were 98 health adults participating in a weight-control intervention study. They recorded their diets in the Gene-Health application, took photographs before and after every meal on the same day, and uploaded them to the Gene-Health application. The amounts of foods and drinks consumed were estimated based on the photographs by trained experts, and the nutrient intakes were calculated using the CAN-Pro 5.0 program, which was named 'Photo Estimation'. The energy and macronutrients estimated from the Gene-Health application were compared with those from a Photo Estimation. The mean differences in energy and macronutrient intakes between the two methods were compared using paired t-test. Results: The mean energy intakes of Gene-Health and Photo Estimation were 1,937.0 kcal and 1,928.3 kcal, respectively. There were no significant differences in intakes of energy, carbohydrate, fat, and energy from fat (%) between two methods. The protein intake and energy from protein (%) of the Gene-Health were higher than those from the Photo Estimation. The energy from carbohydrate (%) for the Photo Estimation was higher than that of the Gene-Health. The Pearson correlation coefficients, weighted Kappa coefficients, and adjacent agreements for energy and macronutrient intakes between the two methods ranged from 0.382 to 0.607, 0.588 to 0.649, and 79.6% to 86.7%, respectively. Conclusion: The Gene-Health application shows acceptable validity as a dietary intake assessment tool for energy and macronutrients. Further studies with female subjects and various age groups will be needed.

• Journal of Science Education
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• v.37 no.3
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• pp.525-537
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• 2013

The purpose of this study was to investigate science integrated process skill of the students in science education center for the gifted. In order to do this, 'free-response test for the assessment of science process skills' developed by Yu-Hyang Kim(2013) was administered to 102 students(15 in elementary school science class, 58 in middle school science class I, and 29 in middle school science class II) who attend the program of science education center for the gifted in C university. The assessment tool measured 9 skills ; formulating inquiry questions, recognizing variables, formulating hypotheses, designing experiment, transforming data, interpreting data, drawing conclusions, formulating generalizations, and evaluating the designed experiments. As a result, the students in science education center for the gifted had relatively high scores in the area of 'formulating hypotheses' and 'recognizing variables', but they had relatively low scores in the area of 'transforming data', 'interpreting data', and 'evaluating the designed experiments'. The 2 items' percentage of correct answers were below 40% ; one is about a drawing a line graph in 'transforming data', and the other requires finding improvements of the experimental design in 'evaluation'. There was no significant difference between boys' scores and girls's one, and between the scores of students in the field of biology and those of students in the other fields(physics, chemistry, and earth science) in science integrated process skills. And there was significant difference according to the periods receiving the gifted education in 'formulating generalizations'. The teaching and learning has to focus on improving science integrated process skills in the program of science education center for the gifted and teaching and learning materials needs to be developed.