• 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.

• Childhood Kidney Diseases
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• v.28 no.1
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• pp.8-15
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• 2024

With the rapid evolution of diagnostic tools, particularly next-generation sequencing, the identification of genetic diseases, predominantly those with pediatric-onset, has significantly advanced. However, this progress presents challenges that span from selecting appropriate tests to the final interpretation of results. This review examines various genetic testing methodologies, each with specific indications and characteristics, emphasizing the importance of selecting the appropriate genetic test in clinical practice, taking into account factors like detection range, cost, turnaround time, and specificity of the clinical diagnosis. Interpretation of variants has become more challenging, often requiring further validation and significant resource allocation. Laboratories primarily classify variants based on the American College of Medical Genetics and Genomics and the Association for Clinical Genomic Science guidelines, however, this process has limitations. This review underscores the critical role of clinicians in matching patient phenotypes with reported genes/variants and considering additional factors such as variable expressivity, disease pleiotropy, and incomplete penetrance. These considerations should be aligned with specific gene-disease characteristics and segregation results based on an extended pedigree. In conclusion, this review aims to enhance understanding of the complexities of clinical genetic testing, advocating for a multidisciplinary approach to ensure accurate diagnosis and effective management of rare genetic diseases.

• Korean Journal of Psychosomatic Medicine
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• v.28 no.2
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• pp.185-193
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• 2020

Objectives : The aim of this study was to develop and to verify the Korean version of the Eating Disorder Diagnosis Scale DSM-5 (K-EDDS) as a web-based diagnostic system, which enables rapid diagnosis of patients for early intervention. Methods : A total of 119 persons participated in the study, including patients with eating disorders (n=38) and college students (n=81). Along with the paper-and-pencil SCOFF, all participants completed the web-based K-EDDS, the Eating Disorder Examination-Questionaire (EDE-Q), and the Clinical Impairment Assessment Questionnaire (CIA). The semi-structured interview using the Eating Disorder Examination Interview (EDE) was conducted for participants with two or more SCOFF scores. Within two weeks, the web-based K-EDDS, the EDE-Q, and the CIA were re-tested. Results : In the exploratory factor analysis, four factors were extracted : body dissatisfaction, binge behaviors, binge frequency and compensatory behaviors. The four subscales of the web-based K-EDDS had significant correlation with each of the four subscales of the EDE-Q. The internal consistency of the web-based K-EDDS was highly satisfactory (Cronbach's alpha=0.93). The diagnostic agreement between the web-based K-EDDS and the EDE was excellent (96.83%), and the web-based K-EDDS's test-retest diagnostic agreement was fairly good (92.86%). The web-based K-EDDS and the CIA also showed significant differences between patients and general population, supporting discriminant validity. Conclusions : This study suggested that the web-based K-EDDS is a valid tool for assisting diagnosis of eating disorders based on DSM-5 in clinical and research fields.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.1028-1037
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• 2023

Objective: To evaluate the computed tomography (CT) features for diagnosing metastatic cervical lymph nodes (LNs) in patients with differentiated thyroid cancer (DTC) and validate the CT-based risk stratification system suggested by the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) guidelines. Materials and Methods: A total of 463 LNs from 399 patients with DTC who underwent preoperative CT staging and ultrasound-guided fine-needle aspiration were included. The following CT features for each LN were evaluated: absence of hilum, cystic changes, calcification, strong enhancement, and heterogeneous enhancement. Multivariable logistic regression analysis was performed to identify independent CT features associated with metastatic LNs, and their diagnostic performances were evaluated. LNs were classified into probably benign, indeterminate, and suspicious categories according to the K-TIRADS and the modified LN classification proposed in our study. The diagnostic performance of both classification systems was compared using the exact McNemar and Kosinski tests. Results: The absence of hilum (odds ratio [OR], 4.859; 95% confidence interval [CI], 1.593-14.823; P = 0.005), strong enhancement (OR, 28.755; 95% CI, 12.719-65.007; P < 0.001), and cystic changes (OR, 46.157; 95% CI, 5.07-420.234; P = 0.001) were independently associated with metastatic LNs. All LNs showing calcification were diagnosed as metastases. Heterogeneous enhancement did not show a significant independent association with metastatic LNs. Strong enhancement, calcification, and cystic changes showed moderate to high specificity (70.1%-100%) and positive predictive value (PPV) (91.8%-100%). The absence of the hilum showed high sensitivity (97.8%) but low specificity (34.0%). The modified LN classification, which excluded heterogeneous enhancement from the K-TIRADS, demonstrated higher specificity (70.1% vs. 62.9%, P = 0.016) and PPV (92.5% vs. 90.9%, P = 0.011) than the K-TIRADS. Conclusion: Excluding heterogeneous enhancement as a suspicious feature resulted in a higher specificity and PPV for diagnosing metastatic LNs than the K-TIRADS. Our research results may provide a basis for revising the LN classification in future guidelines.

• Korean Journal of Radiology
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• v.22 no.5
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• pp.751-758
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• 2021

Objective: Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis. Materials and Methods: In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error. Results: Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65-95%), 100% (41 out of 41; 95% CI, 91-100%), 95% (59 out of 61; 95% CI, 87-98%), and 0.966 (95% CI, 0.912-1.000), respectively. Conclusion: Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.11
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• pp.5433-5438
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• 2012

Background: Hepatocellular carcinoma (HCC) is a common and aggressive malignancy. Despite of the improvements in its treatment, HCC prognosis remains poor due to its recurrence after resection. This study provides complete genetic profile for Egyptian HCC. Genome-wide analyses were performed to identify the predictive signatures. Patients and Methods: Liver tissue was collected from 31 patients with diagnosis of HCC and gene expression levels in the tumours and their adjacent non-neoplastic tissues samples were studied by analyzing changes by microarray then correlate these with the clinico-pathological parameters. Genes were validated in an independent set by qPCR. The genomic profile was associated with genetic disorders and cancer focused on gene expression, cell cycle and cell death. Molecular profile analysis revealed cell cycle progression and arrest at G2/M, but progression to mitosis; unregulated DNA damage check-points, and apoptosis. Result: Nine hundred fifty eight transcripts out of the 25,000 studied cDNAs were differentially expressed; 503 were up-regulated and 455 were down-regulated. A total of 19 pathways were up-regulated through 27 genes and 13 pathways were down-regulated through 19 genes. Thirty-seven genes showed significant differences in their expression between HCC cases with high and low Alpha Feto Protein ($AFP{\geq}600$ IU/ml). The validation for the microarray was done by real time PCR assay in which PPP3CA, ATG-5, BACE genes showed down-regulation and ABCG2, RXRA, ELOVL2, CXR3 genes showed up-regulation. cDNA microarrays showed that among the major upregulated genes in HCC are sets. Conclusion: The identified genes could provide a panel of new diagnostic and prognostic aids for HCC.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.2
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• pp.181-200
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• 2013

The test instrument utilized to diagnose and evaluate a trainee's ability are necessary for an effective information literacy education. Nevertheless, there is a lack of a standardized test instrument to comprehensively measure students' information literacy. The purpose of this study is to develop a standardized test instrument to evaluate the information literacy of middle school students, and to verify the reliability and validity of the test instrument. For this purpose, this study selected factors that can show the information literacy and developed an information literacy test framework that was designed based on Guilford's SOI model and Meeker's SOI-LA test. The test instrument that was developed through this study is a 30-item Web-based multiple-choice test. This study administrated tests in middle school students (794 students joined), and analyzed difficulty, reliability, discrimination index, validity of tests, and reviewed tests items to qualify the standardized test. The cutoff score was also decided when using these tests as a diagnostic information literacy assessment.

• Parasites, Hosts and Diseases
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• v.54 no.3
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• pp.329-334
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• 2016

Trichomoniasis caused by Trichomonas vaginalis is a common sexually transmitted disease. Its association with several health problems, including preterm birth, pelvic inflammatory disease, cervical cancer, and transmission of human immunodeficiency virus, emphasizes the importance of improved access to early and accurate detection of T. vaginalis. In this study, a rapid and efficient loop-mediated isothermal amplification-based method for the detection of T. vaginalis was developed and validated, using vaginal swab specimens from subjects suspected to have trichomoniasis. The LAMP assay targeting the actin gene was highly sensitive with detection limits of 1 trichomonad and 1 pg of T. vaginalis DNA per reaction, and specifically amplified the target gene only from T. vaginalis. Validation of this assay showed that it had the highest sensitivity and better agreement with PCR (used as the gold standard) compared to microscopy and multiplex PCR. This study showed that the LAMP assay, targeting the actin gene, could be used to diagnose early infections of T. vaginalis. Thus, we have provided an alternative molecular diagnostic tool and a point-of-care test that may help to prevent trichomoniasis transmission and associated complications.