• The Korean Journal of Rehabilitation Nursing
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• v.13 no.1
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• pp.5-12
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• 2010

Purpose: The purpose of this study was to examine the psychometric properties of the Korean version of the General Practitioner Assessment of Cognition (K-GPCog) scale. Method: The K-GPCog consists of the 2 subscales, patients and caregivers. Using a sample of 412 community-based Korean older adults, internal consistency reliability was estimated using Cronbach's alpha. To evaluate validity of the K-GPCog, correlational analysis was conducted using Pearson r between K-GPCog and the Korean Dementia Screening Questionnaire (KDSQ). Results: Cronbach's alpha coefficients of the K-GPCog patients' and caregivers' subscales .788 and .794 respectively. Pearson's correlation coefficients were r=-.374, r=-.481, and r=-.493, respectively for the subscales of patients and primary caregivers, and total K-GPCog. The degree of diagnostic agreement about the risk for cognitive disorders of older adults showed 11.7% and 11.2% respectively for the K-GPCog and the KDSQ. Conclusion: The findings provided preliminary evidence of the K-GPCog as a useful screening measure for detecting mild cognitive disorders of Korean older adults. The K-GPCog is particularly useful to identify cognitive disorders from primary caregivers when it is difficult to assess the level of cognition of older adults.

• Nuclear Engineering and Technology
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• v.32 no.3
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• pp.261-273
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• 2000

During an extensive review made as part of the Integrated Diagnosis System project of the Maiden Reactor Project, MOAS (Maryland Operator Advisory System) was identified as one of the most thorough systems developed thus far. MOAS is an integrated on-line diagnosis system that encompasses diverse functional aspects that are required for an effective process disturbance management: (1) intelligent process monitoring and alarming, (2) on-line sensor data validation and sensor failure diagnosis, (3) on-line hardware (besides sensors) failure diagnosis, and (4) real-time corrective measure synthesis. The MOAS methodology was used at the Maiden Man-Machine Laboratory HAMMLAB of the OECD Maiden Reactor Project. The performance of MOAS, developed in G2 real-time expert system shell for the high-pressure preheaters of the NORS process in the HAMMLAB, was tested against a variety of transient scenarios, including failures of the control valves and sensors, and tube leakage of the preheaters. These tests showed that MOAS successfully carried out its intended functions, i.e., quickly recognizing an occurring disturbance, correctly diagnosing its cause, and presenting advice on its control to the operator. The lessons learned and insights gained during the implementation and performance tests also are discussed.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.4
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• pp.375-381
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• 2018

Cytogenetic analysis plays an important role in examinations of a variety of human disorders. Over the years, cytogenetic analysis has evolved to a great extent and become a part of routine laboratory testing; the analysis provides significant diagnostic and prognostic results for human diseases. Microarray in conjunction with molecular cytogenetics and conventional chromosome analysis has transformed the outcomes of clinical cytogenetics. The advantages of microarray technologies have become obvious to the medical and laboratory community involved in genetic diagnosis, resulting in greatly improved visualization and validation capabilities. This article reviews how the field is moving away from conventional cytogenetics towards molecular approaches for the identification of pathogenic genomic imbalances and discusses practical considerations for the routine implementation of these technologies in genetic diagnosis.

• The Plant Pathology Journal
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• v.35 no.3
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• pp.200-207
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• 2019

Fusarium head blight (FHB) is a devastating wheat disease with a significant economic impact. Fhb1 is the most important large effect and stable QTL for FHB resistance. A pore-forming toxin-like (PFT) gene was recently identified as an underlying gene for Fhb1 resistance. In this study, we developed and validated a PFT-based Kompetitive allele specific PCR (KASP) marker for Fhb1. The KASP marker, PFT_KASP, was used to screen 298 diverse wheat breeding lines and cultivars. The KASP clustering results were compared with gelbased gene specific markers and the widely used linked STS marker, UMN10. Eight disagreements were found between PFT_KASP and UMN10 assays among the tested lines. Based on the genotyping and sequencing of genes in the Fhb1 region, these genotypes were found to be common with a previously characterized susceptible haplotype. Therefore, our results indicate that PFT_KASP is a perfect diagnostic marker for Fhb1 and would be a valuable tool for introgression and pyramiding of FHB resistance in wheat cultivars.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.187-195
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• 2022

Purpose: This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs(PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance. Materials and Methods: A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model. Results: The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively. Conclusion: The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the "gold standard" for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.1
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• pp.40-50
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• 2023

Objectives: This study examined the validity of the Childhood Autism Rating Scale, Second Edition (CARS-2) compared with the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) in identifying autism spectrum disorder (ASD). Methods: A total of 237 children were tested using both the CARS-2 and ADOS-2. We examined the correlation using Pearson's correlation analysis. In addition, we used a receiver operating characteristic graph to determine the optimal standard version of the CARS-2 (CARS2-ST) cutoff score for ASD diagnosis using the ADOS-2. Results: The concurrent validity of the CARS2-ST was demonstrated by a significant correlation with the ADOS-2 (r=0.864, p<0.001). The optimal CARS2-ST cutoff scores were 30 and 28.5 for identifying autism and autism spectrum, respectively, based on the ADOS-2. Conclusion: We suggest a newly derived CARS2-ST cutoff score of 28.5 for screening ASD and providing early intervention.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.100-108
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• 2023

In this study, a neural network model inspired by a one-dimensional convolution U-net is developed to automatically accelerate edge localized mode (ELM) detection from big diagnostic data of fusion devices and increase the detection accuracy regardless of the hyperparameter setting. This model recognizes the input signal patterns and overcomes the problems of existing detection algorithms, such as the prominence algorithm and those of differential methods with high sensitivity for the threshold and signal intensity. To train the model, 10 sets of discharge radiation data from the KSTAR are used and sliced into 11091 inputs of length 12 ms, of which 20% are used for validation. According to the receiver operating characteristic curves, our model shows a positive prediction rate and a true prediction rate of approximately 90% each, which is comparable to the best detection performance afforded by other algorithms using their optimized hyperparameters. The accurate and automatic ELM-burst detection methodology used in our model can be beneficial for determining plasma properties, such as the ELM frequency from big data measured in multiple experiments using machines from the KSTAR device and ITER. Additionally, it is applicable to feature detection in the time-series data of other engineering fields.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.49 no.3
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• pp.135-141
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• 2023

Objectives: This study aimed to develop and validate machine learning (ML) models using H₂O-AutoML, an automated ML program, for predicting medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis undergoing tooth extraction or implantation. Patients and Methods: We conducted a retrospective chart review of 340 patients who visited Dankook University Dental Hospital between January 2019 and June 2022 who met the following inclusion criteria: female, age ≥55 years, osteoporosis treated with antiresorptive therapy, and recent dental extraction or implantation. We considered medication administration and duration, demographics, and systemic factors (age and medical history). Local factors, such as surgical method, number of operated teeth, and operation area, were also included. Six algorithms were used to generate the MRONJ prediction model. Results: Gradient boosting demonstrated the best diagnostic accuracy, with an area under the receiver operating characteristic curve (AUC) of 0.8283. Validation with the test dataset yielded a stable AUC of 0.7526. Variable importance analysis identified duration of medication as the most important variable, followed by age, number of teeth operated, and operation site. Conclusion: ML models can help predict MRONJ occurrence in patients with osteoporosis undergoing tooth extraction or implantation based on questionnaire data acquired at the first visit.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1274-1289
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• 2020

Deep learning has recently become one of the most actively researched technologies in the field of medical imaging. The availability of sufficient data and the latest advances in algorithms are important factors that influence the development of deep learning models. However, several other factors should be considered in developing an optimal generalized deep learning model. All the steps, including data collection, labeling, and pre-processing and model training, validation, and complexity can affect the performance of deep learning models. Therefore, appropriate optimization methods should be considered for each step during the development of a deep learning model. In this review, we discuss the important factors to be considered for the optimal development of deep learning models.