• Journal of Veterinary Science
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• v.23 no.5
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• pp.65.1-65.10
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• 2022

Background: Persistent uroliths after a cystotomy in dogs are a common cause of surgical failure. Objectives: This study examined the following: the success rate of retrograde urohydropropulsion in male dogs using non-enhanced computed tomography (CT), whether the CT mean beam attenuation values in Hounsfield Units (mHU) measured in vivo could predict the urolithiasis composition and whether the selected reconstruction kernel may influence the measured mHU. Methods: All dogs and cats that presented with lower urinary tract uroliths and had a non-enhanced CT preceding surgery were included. In male dogs, CT was performed after retrograde urohydropropulsion to detect the remaining urethral calculi. The percentage and location of persistent calculi were recorded. The images were reconstructed using three kernels, from smooth to ultrasharp, and the calculi mHU were measured. Results: Sixty-five patients were included in the study. The success rate of retrograde urohydropropulsion in the 45 male dogs was 55.6% and 86.7% at the first and second attempts, respectively. The predominant components of the calculi were cystine (20), struvite (15), calcium oxalate (8), and urate (7). The convolution kernel influenced the mHU values (p < 0.05). The difference in mHU regarding the calculus composition was better assessed using the smoother kernel. A mHU greater than 1,000 HU was predictive of calcium oxalate calculi. Conclusions: Non-enhanced CT is useful for controlling the success of retrograde urohydropropulsion. The mHU could allow a prediction of the calculus composition, particularly for calcium oxalate, which may help determine the therapeutic strategy.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• The korean journal of orthodontics
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• v.46 no.5
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• pp.331-342
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• 2016

Objective: To evaluate the accuracy and reliability of the diagnostic tools available for assessing maxillary transverse deficiencies. Methods: An electronic search of three databases was performed from their date of establishment to April 2015, with manual searching of reference lists of relevant articles. Articles were considered for inclusion if they reported the accuracy or reliability of a diagnostic method or evaluation technique for maxillary transverse dimensions in mixed or permanent dentitions. Risk of bias was assessed in the included articles, using the Quality Assessment of Diagnostic Accuracy Studies tool-2. Results: Nine articles were selected. The studies were heterogeneous, with moderate to low methodological quality, and all had a high risk of bias. Four suggested that the use of arch width prediction indices with dental cast measurements is unreliable for use in diagnosis. Frontal cephalograms derived from cone-beam computed tomography (CBCT) images were reportedly more reliable for assessing intermaxillary transverse discrepancies than posteroanterior cephalograms. Two studies proposed new three-dimensional transverse analyses with CBCT images that were reportedly reliable, but have not been validated for clinical sensitivity or specificity. No studies reported sensitivity, specificity, positive or negative predictive values or likelihood ratios, or ROC curves of the methods for the diagnosis of transverse deficiencies. Conclusions: Current evidence does not enable solid conclusions to be drawn, owing to a lack of reliable high quality diagnostic studies evaluating maxillary transverse deficiencies. CBCT images are reportedly more reliable for diagnosis, but further validation is required to confirm CBCT's accuracy and diagnostic superiority.

• Journal of Sasang Constitutional Medicine
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• v.34 no.2
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• pp.1-14
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• 2022

Objectives The aim of this study was to investigate the change in Sasang constitution prediction value in 2 years and find the factors associated with it. Methods Cohort data from Korean medicine data center was used. Using Korean Sasang Constitutional Diagnostic Questionnaire (KS-15) which consist of questions related to body shape, temperament, and symptoms, participants were categorized into Tae-Yang (TY), Tae-Eum (TE), So-Yang (SY), and So-Eum (SE). Sasang constitution was assessed on the baseline and after two years. Result Total 5,784 participants were analyzed. (TE 3, 341; SE 911; SY 1,532). Among them, 1,402 participants (24.2%) showed different prediction value in KS-15 after two years. The proportion of participants showing different prediction value in two years was the highest in SY, and the lowest in TE group. The factors associated with the change in Sasang constitution prediction value were different by constitution type. The change in feeling after sweating was significantly associated with the change in prediction value in TE and SY groups, not in SE group. Although temperament was not significantly associated with the change in prediction value from TE to SE, it was significantly associated with that in the change from TE to SY. The change in BMI and appetite were associated with the change in constitution prediction value in all three constitution types. Conclusion Although the factors associated with the change in prediction value of Sasang constitution were different by each constitution type, BMI and appetite were significant in all three types. These factors could be useful for developing Sasang constitution questionnaire and deciding re-prediction needs of Sasang constitution. Further research about the factors related to Sasang constitution diagnosis need to be conducted.

• Tuberculosis and Respiratory Diseases
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• v.64 no.4
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• pp.266-271
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• 2008

Background: Estimation of the probability of a patient having an acute pulmonary embolism (PE) for patients with a suspected PE are well established in North America and Europe. However, an assessment of the prediction rules for a PE has not been clearly defined in Korea. The aim of this study is to assess the prediction rules for patients with a suspected PE in Korea. Methods: We performed a retrospective study of 210 inpatients or patients that visited the emergency ward with a suspected PE where computed tomography pulmonary angiography was performed at a single institution between January 2005 and March 2007. Simplified Wells rules and revised Geneva rules were used to estimate the clinical probability of a PE based on information from medical records. Results: Of the 210 patients with a suspected PE, 49 (19.5%) patients had an actual diagnosis of a PE. The proportion of patients classified by Wells rules and the Geneva rules had a low probability of 1% and 21%, an intermediate probability of 62.5% and 76.2%, and a high probability of 33.8% and 2.8%, respectively. The prevalence of PE patients with a low, intermediate and high probability categorized by the Wells rules and Geneva rules was 100% and 4.5% in the low range, 18.2% and 22.5% in the intermediate range, and 19.7% and 50% in the high range, respectively. Receiver operating characteristic curve analysis showed that the revised Geneva rules had a higher accuracy than the Wells rules in terms of detecting PE. Concordance between the two prediction rules was poor ($\kappa$ coefficient=0.06). Conclusion: In the present study, the two prediction rules had a different predictive accuracy for pulmonary embolisms. Applying the revised Geneva rules to inpatients and emergency ward patients suspected of having PE may allow a more effective diagnostic process than the use of the Wells rules.

• Korean Journal of Radiology
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• v.22 no.9
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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.

• The Journal of Korean Orthopaedic Ultrasound Society
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• v.6 no.2
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• pp.53-59
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• 2013

Purpose: The aim of this study wasto evaluate the comparison of CT arthrography and ultrasonography, confirmed by the arthroscopic finding in patients with rotator cuff disease. Materials and Methods: We evaluated fifty seven patients with rotator cuff disease underwent CTA and arthroscopy, and twenty eight patients had taken ultrasonographyadditionally. The diagnostic value and prediction for tear size between CTA and ultrasonography were evaluated, as compared to arthroscopic findings. Results: CTA showed a sensitivity of 86.2% and a specificity of 100% in full thickness tear ofsupraspinatus, a sensitivity of 58.3% and a specificity of 87.8% in partial-thickness tear. CTA demonstrated good diagnostic value for full thickness tear, but there was relatively lower value for partial-thickness tear. Ultrasonography showed a sensitivity of 84.6% and a specificity of 86.7% for diagnosing in full thickness tear, a sensitivity of 84.6% and a specificity of 73.3% in partial-thickness tear. Ultrasonography provided good diagnostic value, but, there is lesser accurate result for prediction of tear size. Conclusion: CTA showedgood diagnostic tool of detection full-thickness tear of rotator cuff disease and predicting of tear size. Comparing with ultrasonography, CTA was inferior for detection of partial-thickness tear, but, provided better estimation for tear size.

• ETRI Journal
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• v.37 no.2
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• pp.222-232
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• 2015

In this paper, we propose a dual-phase approach to improve the process of heart disease prediction in a mobile environment. Firstly, only the confident frequent rules are extracted from a patient's clinical information. These are then used to foretell the possibility of the presence of heart disease. However, in some cases, subjects cannot describe exactly what has happened to them or they may have a silent disease - in which case it won't be possible to detect any symptoms at this stage. To address these problems, data records collected over a long period of time of a patient's heart rate variability (HRV) are used to predict whether the patient is suffering from heart disease. By analyzing HRV patterns, doctors can determine whether a patient is suffering from heart disease. The task of collecting HRV patterns is done by an online artificial neural network, which as well as learning knew knowledge, is able to store and preserve all previously learned knowledge. An experiment is conducted to evaluate the performance of the proposed heart disease prediction process under different settings. The results show that the process's performance outperforms existing techniques such as that of the self-organizing map and gas neural growing in terms of classification and diagnostic accuracy, and network structure.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.510-518
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• 2022

This paper confirmed the technical reliability of mobile-based sarcopenia prediction and monitoring system. In implementing the developed system, we designed using only sensors built into a smartphone without a separate external device. The prediction system predicts the possibility of sarcopenia without visiting a hospital by performing the SARC-F survey, the 5-time chair stand test, and the rapid tapping test. The Monitoring system tracks and analyzes the average walking speed in daily life to quickly detect the risk of sarcopenia. Through this, it is possible to rapid detection of undiagnosed risk of undiagnosed sarcopenia and initiate appropriate medical treatment. Through prediction and monitoring system, the user may predict and manage sarcopenia, and the developed system can have a positive effect on reducing medical demand and reducing medical costs. In addition, collected data is useful for the patient-doctor communication. Furthermore, the collected data can be used for learning data of artificial intelligence, contributing to medical artificial intelligence and e-health industry.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.