• Asian Pacific Journal of Cancer Prevention
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• 제16권1호
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• pp.201-204
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• 2015

Background: This study was designed to determine the accuracy of bronchoalveolar lavage fluid cytology (BAL) using histopathologic examination of transbronchial biopsy specimens as the gold standard in diagnosis of lung carcinoma at our center. Materials and Methods: A retrospective study was conducted to investigate a total of 388 patients who were suspected of having lung cancer and had undergone fiberoptic bronchoscopy in Shahid Sadoughi hospital from 2006 to 2011. Lung masses were proven to be malignant by histology. Results: Transbronchial lung biopsy (TBLB) identified malignancy in 183 of the 388 cases, including 48 cases (26.2%) with adenocarcinoma, 4(2.1%) with bronchioloalveolar carcinoma, 47(25.6%)with squamous cell carcinoma, 34(18.5%) with well-diffentiated neuroendocrine carcinoma, 35(19.1%) with small cell carcinoma, 14 (7.6%) with non-small cell carcinoma, and 1 (0.54%) with large cell carcinoma. A total of 205 cases were correctly classified as negative. BAL was also performed in 388 patients; 86/103 cases were consistent with the final diagnosis of lung cancer and 188/285 cases were correctly classified as negative. The sensitivity of BAL was 46.9%(CI:41.9%, 51.8%)) and its specificity was 91.6%(CI:88.8%, 94.3%). BAL had a positive predictive value (PPV) of 83.4%(CI:79.7%, 87.1%) and a negative predictive value (NPV) of 65.8%(CI:61%, 70.5%). The overall accuracy of BAL was 70.5% and the exact concordance was 39%. Conclusions: Our findings suggest that BAL cytology is not sensitive but is a specific test for diagnosis of lung carcinoma. If transbronchial lung biopsy is combined with bronchoalveolar lavage, the positive diagnostic rate will be further elevated.

• Asian Pacific Journal of Cancer Prevention
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• 제15권13호
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• pp.5245-5247
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• 2014

Background: Helicobacter pylori (H.pylori) is one of the most important causes of dyspepsia and gastric cancer and diagnosis can be made by invasive or non-invasive methods. The Atlas Helicobacter pylori antigen test is a new rapid non-invasive method which is simple to conduct. The aim of this study was to determine its sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. Materials and Methods: This prospective study was conducted between July 2012 and December 2013. Stool samples of 59 dyspeptic patients who underwent upper endoscopy were evaluated for H. pylori stool antigen. Results: From the 59 patients who participated in this study, there were 36 (61%) males and 23 (39%) females. H. pylori was diagnosed in 24 (40.7%) gastric biopsies, 22 (91.7 %) of these being positive for the Atlas H. pylori antigen test. The sensitivity, specificity, PPV, NPV and accuracy were 91.7%, 100%, 100%, 94.6% and 96.6% respectively. Conclusions: The Atlas H. pylori antigen test is a new non-invasive method which is simple to perform and avails reliable results in a few minutes. Thus it can be the best option for the diagnosis of H. pylori infection due to its high sensitivity and specificity.

• The Journal of Advanced Prosthodontics
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• 제12권2호
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• pp.49-54
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• 2020

PURPOSE. The purpose of this study was to compare the performance of Accura to that of the T-scan for indicating occlusal contacts. MATERIALS AND METHODS. Twenty-four subjects were selected. Their maxillary dental casts were scanned with a model scanner. The Stereolithography files of the casts were positioned to align with the occlusal plane. Occlusal surfaces of every tooth were divided into three to six anatomic regions. T-scan and Accura recordings were made during two masticatory cycles. The T-scan and Accura images were captured at the maximum bite force and overlapped to the cast. Photographs of interocclusal records were used as the reference during overlap. The occlusal contacts were counted to compare the T-scan and Accura. McNemar's test was used for statistical significance and the corresponding P-values were calculated from a chi-square distribution with one degree of freedom. The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of Accura were calculated relative to the T-scan values as a control. RESULTS. No statistical differences (P>.05) were found between the T-scan and Accura methods. The accuracy of Accura was 75.8%, sensitivity was 82.1%, specificity was 60.1%, PPV was 82.9%, and NPV was 60.1%. CONCLUSION. Accura could be another possible option as a computerized occlusal analysis system for indicating occlusal contacts at maximum intercuspation.

• KIEAE Journal
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• 제17권5호
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2008년도 춘계학술발표논문집
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• pp.248-250
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• 2008

본 논문에서는 교통사고를 예방하고 교통사고 구간 대기시간을 줄이기 위해서 신경망을 이용한 예측방법을 제안한다. 뿐만 아니라, 교통사고 예측에 있어서 신경망에 정규화하지 않은 데이터를 사용하는 방법을 제시한다. 이 방법은 신경망 훈련시 데이타의 최대 값을 추정할 필요가 없어 정규화 없이 신경망을 사용 가능하며, 신뢰성 예측 결과도 추정된 최대 값과 실제 획득될 최대 값과의 차이(추정 오차)만큼 정확해질 수 있다. 또한 비정규화 된 데이터를 사용하는 방법이 데이터의 최대값을 알고 있다고 가정한 상태의 정규화된 방법보다 예측 정확성이 좋음을 보였다. 모의실험결과 제안된 신경망 예측시스템이 신경망을 고려하지 않은 기존방법보다 교통사고 구간 대기시간을 줄일 수 있음을 입증했다. 이와 같이 검증된 예측능력을 바탕으로 사용자에게 교통상황을 실시간으로 서비스하기 위하여 센서 데이터베이스를 이용한 실시간 교통정보 예보 시스템을 제안한다.

• Journal of Gastric Cancer
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• 제21권3호
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• pp.246-257
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• 2021

Purpose: It is unclear whether high-definition (HD) imaging improves visibility and diagnostic ability in early gastric cancer (EGC) compared with standard-definition (SD) imaging. We aimed to compare the diagnostic performance and visibility scores of HD and SD ultraslim endoscopes in EGC. Materials and Methods: We used HD and SD ultraslim endoscopes to obtain 60 images with similar compositions of gastric environments. Of the 60 images, 30 showed EGC (15 images for each modality) and 30 showed no EGC (15 images for each modality). Seventeen endoscopists evaluated the presence and location of the lesions in each image. Diagnostic ability was compared between modalities. The color difference between a lesion and the surrounding mucosa (ΔE) was measured and compared between the modalities. Results: The ability of HD to detect EGC was significantly higher than that of SD (accuracy: 80.8% vs. 71.6%, P=0.017; sensitivity: 94.9% vs. 76.5%, P<0.001; positive predictive value, 76.2% vs. 55.3%, P<0.001; and negative predictive value (NPV), 94.1% vs. 73.5%, P<0.001). The ability of HD to determine the horizontal extent of EGC was significantly higher than that of SD (accuracy: 71.0% vs. 57.8%, P=0.004; sensitivity: 75.3% vs. 49.0%, P<0.001; NPV, 72.9% vs. 55.9%, P<0.001; and area under the curve: 0.891 vs. 0.631, P=0.038). The mean ΔE was significantly higher for HD than for SD (10.3 vs. 5.9, P=0.011). Conclusions: The HD ultraslim endoscope showed a higher diagnostic performance in EGC than the SD endoscope because it provided good color contrast.

• The Korean Journal of Pain
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• 제34권3호
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• pp.339-345
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• 2021

Background: Ultrasound-guided caudal epidural injection (CEI) is limited in that it cannot confirm drug distribution at the target site without fluoroscopy. We hypothesized that visualization of solution flow through the inter-laminar space of the lumbosacral spine using color Doppler ultrasound alone would allow for confirmation of drug distribution. Therefore, we aimed to prospectively evaluate the usefulness of this method by comparing the color Doppler image in the paramedian sagittal oblique view of the lumbosacral spine (LS-PSOV) with the distribution of the contrast medium observed during fluoroscopy. Methods: Sixty-five patients received a 10-mL CEI of solution containing contrast medium under ultrasound guidance. During injection, flow was observed in the LSPSOV using color Doppler ultrasonography, following which it was confirmed using fluoroscopy. The presence of contrast image at L5-S1 on fluoroscopy was defined as "successful CEI." We then calculated prediction accuracy for successful CEI using color Doppler ultrasonography in the LS-PSOV. We also investigated the correlation between the distribution levels measured via color Doppler and fluoroscopy. Results: Prediction accuracy with color Doppler ultrasonography was 96.9%. The sensitivity, specificity, positive predictive value, and negative predictive value were 96.7%, 100%, 100%, and 60.0%, respectively. In 52 of 65 patients (80%), the highest level at which contrast image was observed was the same for both color Doppler ultrasonography and fluoroscopy. Conclusions: Our findings demonstrate that color Doppler ultrasonography in the LS-PSOV is a new method for determining whether a drug solution reaches the lumbosacral region (i.e., the main target level) without the need for fluoroscopy.

• 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.

• 산업경영시스템학회지
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• 제45권1호
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• pp.71-82
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• 2022

Most of the predictions using machine learning are neutral predictions considering the symmetrical situation where the predicted value is not smaller or larger than the actual value. However, in some situations, asymmetric prediction such as over-prediction or under-prediction may be better than neutral prediction, and it can induce better judgment by providing various predictions to decision makers. A method called Asymmetric Twin Support Vector Regression (ATSVR) using TSVR(Twin Support Vector Regression), which has a fast calculation time, was proposed by controlling the asymmetry of the upper and lower widths of the ε-tube and the asymmetry of the penalty with two parameters. In addition, by applying the existing GSVQR and the proposed ATSVR, prediction using the prediction propensities of over-prediction, under-prediction, and neutral prediction was performed. When two parameters were used for both GSVQR and ATSVR, it was possible to predict according to the prediction propensity, and ATSVR was found to be more than twice as fast in terms of calculation time. On the other hand, in terms of accuracy, there was no significant difference between ATSVR and GSVQR, but it was found that GSVQR reflected the prediction propensity better than ATSVR when checking the figures. The accuracy of under-prediction or over-prediction was lower than that of neutral prediction. It seems that using both parameters rather than using one of the two parameters (p_1,p_2) increases the change in the prediction tendency. However, depending on the situation, it may be better to use only one of the two parameters.

• 산업경영시스템학회지
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• 제46권1호
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• pp.105-111
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• 2023

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.