• 대한의용생체공학회:의공학회지
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• 제29권3호
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• pp.198-204
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• 2008

Uroflowmetry is a non-invasive clinical test useful for screening benign prostatic hyperplasia(BPH) common in the aged men. The current standard way to obtain the urinary flow rate is to continuously acquire the urine weight signal proportional to volume over time. The present study proposed an alternative technique measuring pressure to overcome noise problems present in the standard weight measuring technique. Experiments were performed to simultaneously acquire both weight and pressure changes during urination of 9 normal men. Noise components were separated from volume signals converted from both weight and pressure signals based on the polynomial signal model. Signal-to-noise ratio was defined as the ratio of the energies between signal and noise components of the measured volume changes, which was 8.5 times larger in the pressure measuring technique, implying that cleaner signal could be obtained, more immune to noisy environments. When four important diagnostic parameters were estimated, excellent correlation coefficients higher than 0.99 were resulted with mean relative errors less than 5%. Therefore, the present pressure measurement seemed valid as an alternative technique for uroflowmetry.

• 비파괴검사학회지
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• 제36권3호
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• pp.217-224
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• 2016

콘크리트는 건축물에 가장 많이 사용되는 재료 중 하나이다. 건축물 시공 시 적절한 하중 재하시점을 결정하기 위해 콘크리트의 경화 상태의 점검은 매우 중요한 사항이다. 또한 부정확한 경화 모니터링은 건축물의 부실공사 혹은 붕괴로 이어질 수 있다. 본 연구에서는 어드미턴스를 기반으로 한 압전체 센서 자가진단기법을 확장 적용한 콘크리트 경화 모니터링 기법을 개발하였다. 이 기법을 통해 콘크리트의 경화를 모니터링 하였으며 실험 결과 분석을 통해 본 기법의 상대강도 추정 가능성을 확인하였다. 또한 경화 시 발현강도에 따른 어드미턴스 신호 예측을 위해 수치적 모델링을 하였으며 실험 결과와 경화 진행 경향성을 비교하였다. 이를 통해 본 연구에서 개발한 기법의 효용성을 실험 및 수치적으로 확인하였다.

• Journal of Korean Medical Science
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• 제33권53호
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• pp.342.1-342.6
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• 2018

We validated the diagnostic performance of a previously developed blood-based 7-protein biomarker panel, $AptoDetect^{TM}$-Lung (Aptamer Sciences Inc., Pohang, Korea) using modified aptamer-based proteomic technology for lung cancer detection. Non-small cell lung cancer (NSCLC), 200 patients and benign nodule controls, 200 participants were enrolled. In a high-risk population corresponding to ${\geq}55years$ of age and ${\geq}30pack-years$, the diagnostic performance was improved, showing 73.3% sensitivity and 90.5% specificity with an area under the curve of 0.88. $AptoDetect^{TM}$-Lung (Aptamer Sciences Inc.) offers the best validated performance to discriminate NSCLC from benign nodule controls in a high-risk population and could play a complementary role in lung cancer screening.

• 기독교교육논총
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• 제64권
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• pp.409-444
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• 2020

본 연구는 대학의 학업 활동에 영향을 미치는 요인들에 대하여 정확하게 진단하기 위한 측정 도구를 개발하여 제안하고자 하는 것이다. 이를 위해 대학생들의 학업 생활에 영향을 미치는 요인들로 평가 영역을 구성하고, 그 도출된 영역들을 진단할 수 있는 도구를 개발하여 학생들의 대학생활 적응에 적절한 도움을 줄 수 있는 근거를 마련하고자 하였다. 이 도구는 선행 연구를 통해 문항을 구성하였고, 델파이 연구를 통해 초안을 개발하였다. 개발된 도구는 K대학의 신입생 182명의 응답값을 분석하여 신뢰도와 타당성을 검증하였다. 분석의 결과 신뢰도는 평가 영역별로 신뢰도 .869~.955로 높은 신뢰도를 보였으며, 문항-총점간 상관분석의 결과 대부분의 문항이 .30~.80사이로 적절하였고, .80이 넘는 문항은 다중공선성값 10 이하로 적절하였다. 탐색적 요인분석의 결과로 도출된 요인과 문항간의 관계를 토대로 확인적 요인분석을 실시 및 검증하여 최종도구 개발 및 제안하였다.

• 대기
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• 제31권5호
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• pp.473-488
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• 2021

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

• Journal of Veterinary Science
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• 제21권1호
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• pp.16.1-16.10
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• 2020

Cancer is a major cause of death in dogs worldwide, and the incidence of cancer in dogs is increasing. The attenuated total reflection Fourier transform infrared spectroscopic (ATR-FTIR) technique is a powerful tool for the diagnosis of several diseases. This method enables samples to be examined directly without pre-preparation. In this study, we evaluated the diagnostic value of ATR-FTIR for the detection of cancer in dogs. Cancer-bearing dogs (n = 30) diagnosed by pathologists and clinically healthy dogs (n = 40) were enrolled in this study. Peripheral blood was collected for clinicopathological diagnosis. ATR-FTIR spectra were acquired, and principal component analysis was performed on the full wave number spectra (4,000-650 cm^-1). The leave-one-out cross validation technique and partial least squares regression analysis were used to predict normal and cancer spectra. Red blood cell counts, hemoglobin levels and white blood cell counts were significantly lower in cancer-bearing dogs than in clinically healthy dogs (p < 0.01, p < 0.01 and p = 0.03, respectively). ATR-FTIR spectra showed significant differences between the clinically healthy and cancer-bearing groups. This finding demonstrates that ATR-FTIR can be applied as a screening technique to distinguish between cancer-bearing dogs and healthy dogs.

• International Journal of Computer Science & Network Security
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• 제23권8호
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• pp.17-25
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• 2023

The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.

• Parasites, Hosts and Diseases
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• 제61권2호
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• pp.202-209
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• 2023

Lophomonas blattarum is an anaerobic protozoan living in the intestine of cockroaches and house dust mites, with ultramicroscopic characteristics such as the presence of a parabasal body, axial filament, and absence of mitochondria. More than 200 cases of Lophomonas infection of the respiratory tract have been reported worldwide. However, the current diagnosis of such infection depends only on light microscopic morphological findings from respiratory secretions. In this study, we attempted to provide more robust evidence of protozoal infection in an immunocompromised patient with atypical pneumonia, positive for Lophomonas-like protozoal cell forms. A direct search of bronchoalveolar lavage fluid via polymerase chain reaction (PCR), transmission electron microscopy (TEM), and metagenomic next-generation sequencing did not prove the presence of protozoal infection. PCR results were not validated with sufficient rigor, while de novo assembly and taxonomic classification results did not confirm the presence of an unidentified pathogen. The TEM results implied that such protozoal forms in light microscopy are actually non-detached ciliated epithelial cells. After ruling out infectious causes, the patient's final diagnosis was drug-induced pneumonitis. These findings underscore the lack of validation in the previously utilized diagnostic methods, and more evidence in the presence of L. blattarum is required to further prove its pathogenicity.

• Korean Journal of Radiology
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• 제22권9호
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• pp.1481-1489
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• 2021

Objective: To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer. Materials and Methods: This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation. Results: A total of 157 patients (median age, 56 years; range, 27-79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62-0.82). Conclusion: Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제12권3호
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• pp.119-126
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• 2023

근감소증은 국내는 2021년 질병으로 분류되었을 만큼 잘 알려져 있지 않지만 고령화사회에 진입한 선진국에서는 사회적 문제로 인식하고 있다. 근감소증 진단은 유럽노인근감소증 진단그룹(EWGSOP)과 아시아근감소증진단그룹(AWGS)에서 제시하는 국제표준지침을 따른다. 최근 진단방법으로 절대적 근육량 이외에 신체수행평가로 보행속도 측정과 일어서기 검사 등을 통하여 근육 기능을 함께 측정할 것을 권고하고 있다. 근육량을 측정하기 위한 대표적인 방법으로 DEXA를 이용한 체성분 분석 방법이 임상에서 정식으로 실시하고 있다. 또한 MRI 또는 CT의 복부 영상을 이용하여 근육량을 측정하는 다양한 연구가 활발하게 진행되고 있다. 따라서 본 논문에서는 근감소증 진단을 위해서 비교적 짧은 촬영시간을 갖는 CT의 복부영상기반으로 AI 영상 분할 모델을 개발하고 다기관 검증한 내용을 기술한다. 우리는 CT 영상 중에 요추의 L3 영역을 분류하여 피하지방, 내장지방, 근육을 자동으로 분할할 수 있는 인공지능 모델을 U-Net 모델을 사용하여 개발하였다. 또한 모델의 성능평가를 위해서 분할영역의 IOU(Intersection over Union)를 계산하여 내부검증을 진행했으며, 타 병원의 데이터를 활용하여 동일한 IOU 방법으로 외부검증을 진행한 결과를 보인다. 검증 결과를 토대로 문제점과 해결방안에 대해서 검증하고 보완하고자 했다.