• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2507-2511
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• 2016

Purpose: To study the diagnostic accuracies of serum human epididymis protein 4 (HE-4) levels, virtual organ computer-aided analysis (VOCAL) parameters and endometrial volume in endometrial cancer cases. Materials and Methods: One hundred and seven patients (37 with endometrial cancer and 70 with benign endometrial pathology) were included in this study. VOCAL parameters and serum HE-4 levels were compared between the groups. Results: Area under the curve (AUC) values were 0.702, 0.658, 0.706 for vascularization index (VI), the flow index (FI) and the vascularization flow index (VFI), respectively. A cut off value of 0.568 for VI demonstrated 70% sensitivity, 72% specificity, 56% positive predictive value (PPV) and a81% negative predictive value (NPV). A cut off value of 25.8 for showed a senitivith of 70% and a specificity of 58% with aPPV of 46% and NPV of 78%, and with a cut off value of 0.12 for VFI 70%, 69%, 54% and 81%, respectively. The area under the curve for HE-4 was 0.814. A cut off value of 458 pmol/L was predictive of malignancy with 86% sensitivity and 63% specificity. Conclusions: VOCAL parameters and serum HE-4 levels were statistically significantly higher in the endometrial cancer patients. Serum HE-4 levels provided a greater sensitivity compared to power doppler angiography for predicting malignancy or benign endometrial pathology.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.4
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• pp.89-97
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• 2014

As the digital control technologies in automotive industry have advanced, electronic control units(ECUs) play a key-role to improve system performance. Transmission control unit(TCU) is a shifting controller for automatic transmission of which major functions are to determine the shift and manage the shifting process considering the various sensor signal on transmission and driver's commands. As with any ECU in vehicle, TCU performs complex algorithms such as shift control, diagnostic and failsafe functions. However, firmware design analysis is hardly possible by the reverse engineering due to code protection. Transmission simulator is a hardware-in-the-loop simulator which enables TCU to work in normal mode by simulating the electrical signal of TCU interface. In this research, diagnosis and failsafe algorithm implemented on commercialized TCU is analyzed by using the transmission simulator that is developed for wheel loader construction vehicle. This paper gives various experimental results on the proportional solenoid current trajectories for different operating modes, error detection criterion and limphome mode gears for all the possible cases of clutch malfunction. The derived results for conventional TCU can be applied to the development of inherent TCU algorithms and the transmission simulator can also be utilized for the test of TCU to be developed.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.43-50
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• 2017

According to living environment, the number of deaths due to thyroid diseases increased. In this paper, we proposed an algorithm for recognizing a thyroid detection using texture analysis based on shape, gray level co-occurrence matrix and gray level run length matrix. First of all, we segmented the region of interest (ROI) using active contour model algorithm. Then, we applied a total of 18 features (5 first order descriptors, 10 Gray level co-occurrence matrix features(GLCM), 2 Gray level run length matrix features and shape feature) to each thyroid region of interest. The extracted features are used as statistical analysis. Our results show that first order statistics (Skewness, Entropy, Energy, Smoothness), GLCM (Correlation, Contrast, Energy, Entropy, Difference variance, Difference Entropy, Homogeneity, Maximum Probability, Sum average, Sum entropy), GLRLM features and shape feature helped to distinguish thyroid benign and malignant. This algorithm will be helpful to diagnose of thyroid nodule on ultrasound images.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Journal of Gastric Cancer
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• v.24 no.3
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• pp.327-340
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• 2024

Purpose: Results of initial endoscopic biopsy of gastric lesions often differ from those of the final pathological diagnosis. We evaluated whether an artificial intelligence-based gastric lesion detection and diagnostic system, ENdoscopy as AI-powered Device Computer Aided Diagnosis for Gastroscopy (ENAD CAD-G), could reduce this discrepancy. Materials and Methods: We retrospectively collected 24,948 endoscopic images of early gastric cancers (EGCs), dysplasia, and benign lesions from 9,892 patients who underwent esophagogastroduodenoscopy between 2011 and 2021. The diagnostic performance of ENAD CAD-G was evaluated using the following real-world datasets: patients referred from community clinics with initial biopsy results of atypia (n=154), participants who underwent endoscopic resection for neoplasms (Internal video set, n=140), and participants who underwent endoscopy for screening or suspicion of gastric neoplasm referred from community clinics (External video set, n=296). Results: ENAD CAD-G classified the referred gastric lesions of atypia into EGC (accuracy, 82.47%; 95% confidence interval [CI], 76.46%-88.47%), dysplasia (88.31%; 83.24%-93.39%), and benign lesions (83.12%; 77.20%-89.03%). In the Internal video set, ENAD CAD-G identified dysplasia and EGC with diagnostic accuracies of 88.57% (95% CI, 83.30%-93.84%) and 91.43% (86.79%-96.07%), respectively, compared with an accuracy of 60.71% (52.62%-68.80%) for the initial biopsy results (P<0.001). In the External video set, ENAD CAD-G classified EGC, dysplasia, and benign lesions with diagnostic accuracies of 87.50% (83.73%-91.27%), 90.54% (87.21%-93.87%), and 88.85% (85.27%-92.44%), respectively. Conclusions: ENAD CAD-G is superior to initial biopsy for the detection and diagnosis of gastric lesions that require endoscopic resection. ENAD CAD-G can assist community endoscopists in identifying gastric lesions that require endoscopic resection.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.2
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• pp.222-230
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• 2022

Digital technology is gradually expanding its field and has a great influence on various fields of dentistry. Recently in digital dentistry, the importance of superimposing various 3-dimensional (3D) image data is emerging, in order to utilize gathered data effectively for diagnosis and prosthesis fabrication. Integrating data from facial scans, intraoral scans, and mandibular movement recordings can create a virtual patient. A virtual patient is formed by integrating digital 3D diagnostic data such as intraoral and extraoral soft tissues, residual dentition, and dynamic occlusion, and the results of prosthetic treatment can be evaluated virtually. The patients in this case report were a 37-year-old female whose chief complaint is that the appearance of the existing prosthesis was distorted and a 55-year-old female patient whose anterior prosthesis needed to be refabricated after the endodontic treatment. 3D facial scans were obtained from each patient, and the patient's mandibular movements were recorded using ARCUS Digma 2 (KaVo Dental GmbH, Biberach an der Riss, Germany). The collected data were integrated on computer-aided design (CAD) software (Exocad dental CAD; exocad GmbH, Darmstadt, Germany) and transferred to a virtual articulator to create a digital virtual patient. The temporary fixed prostheses were designed, restored, and evaluated, and it was reflected into the final restorations. With the aid of the virtual dental patient, accuracy and predictability could be increased throughout treatment, simplifying the occlusal adjustment and clinical evaluation with improved esthetic outcomes.

• Korean Journal of Radiology
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• v.24 no.3
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• pp.259-270
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• 2023

Objective: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) interpretation in real-world clinical practice. We aimed to compare the accuracy of CR interpretation assisted by AI-CAD to that of conventional interpretation in patients who presented to the emergency department (ED) with acute respiratory symptoms using a pragmatic randomized controlled trial. Materials and Methods: Patients who underwent CRs for acute respiratory symptoms at the ED of a tertiary referral institution were randomly assigned to intervention group (with assistance from an AI-CAD for CR interpretation) or control group (without AI assistance). Using a commercial AI-CAD system (Lunit INSIGHT CXR, version 2.0.2.0; Lunit Inc.). Other clinical practices were consistent with standard procedures. Sensitivity and false-positive rates of CR interpretation by duty trainee radiologists for identifying acute thoracic diseases were the primary and secondary outcomes, respectively. The reference standards for acute thoracic disease were established based on a review of the patient's medical record at least 30 days after the ED visit. Results: We randomly assigned 3576 participants to either the intervention group (1761 participants; mean age ± standard deviation, 65 ± 17 years; 978 males; acute thoracic disease in 472 participants) or the control group (1815 participants; 64 ± 17 years; 988 males; acute thoracic disease in 491 participants). The sensitivity (67.2% [317/472] in the intervention group vs. 66.0% [324/491] in the control group; odds ratio, 1.02 [95% confidence interval, 0.70-1.49]; P = 0.917) and false-positive rate (19.3% [249/1289] vs. 18.5% [245/1324]; odds ratio, 1.00 [95% confidence interval, 0.79-1.26]; P = 0.985) of CR interpretation by duty radiologists were not associated with the use of AI-CAD. Conclusion: AI-CAD did not improve the sensitivity and false-positive rate of CR interpretation for diagnosing acute thoracic disease in patients with acute respiratory symptoms who presented to the ED.

• Proceedings of the Korean Vacuum Society Conference
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• 2015.08a
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• pp.61.1-61.1
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• 2015

Nanoscale semiconductor plasma processing has become one of the most challenging issues due to the limits of physicochemical fabrication routes with its inherent complexity. The mission of future and emerging plasma processing for development of next generation semiconductor processing is to achieve the ideal nanostructures without abnormal profiles and damages, such as 3D NAND cell array with ultra-high aspect ratio, cylinder capacitors, shallow trench isolation, and 3D logic devices. In spite of significant contributions of research frontiers, these processes are still unveiled due to their inherent complexity of physicochemical behaviors, and gaps in academic research prevent their predictable simulation. To overcome these issues, a Korean plasma consortium began in 2009 with the principal aim to develop a realistic and ultrafast 3D topography simulator of semiconductor plasma processing coupled with zero-D bulk plasma models. In this work, aspects of this computational tool are introduced. The simulator was composed of a multiple 3D level-set based moving algorithm, zero-D bulk plasma module including pulsed plasma processing, a 3D ballistic transport module, and a surface reaction module. The main rate coefficients in bulk and surface reaction models were extracted by molecular simulations or fitting experimental data from several diagnostic tools in an inductively coupled fluorocarbon plasma system. Furthermore, it is well known that realistic ballistic transport is a simulation bottleneck due to the brute-force computation required. In this work, effective parallel computing using graphics processing units was applied to improve the computational performance drastically, so that computer-aided design of these processes is possible due to drastically reduced computational time. Finally, it is demonstrated that 3D feature profile simulations coupled with bulk plasma models can lead to better understanding of abnormal behaviors, such as necking, bowing, etch stops and twisting during high aspect ratio contact hole etch.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.76-81
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• 2023

To distinguish cancer cells from normal cells, H&E (Hematoxylin & Eosin) staining is required. Pathological staining requires a lot of money and time. Recently, a digital dyeing method has been introduced to reduce such cost and time. In this paper, we propose a novel digital pathology algorithms. The first algorithm is the Pair method. This method learns the dyed phase image and unstained amplitude image taken by FPM (Fourier Ptychographic Microscopy) and converts it into a dyed amplitude image. The second algorithm is the unpair method. This method use the stained and unstained fluorescence microscopic images for modeling. In this study, digital staining was performed using a generative adversarial network (GAN). From the experimental results, we noticed that both the pair and unpair algorithms shows the excellent performance.

• The Journal of Advanced Prosthodontics
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• v.16 no.3
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• pp.139-150
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• 2024

PURPOSE. The purpose of this diagnostic study was to assess the accuracy and time efficiency of a digital method to draw the denture foundation extension outline on preliminary casts compared with the conventional technique. MATERIALS AND METHODS. A total of 28 preliminary edentulous casts with no anatomical landmarks were digitized using a laboratory scanner. The outlining of the entire basal seat of the denture was performed on preliminary casts and digitized. Casts with no extension outline were digitized and outlines were drawn using software (DWOS, Straumann). The accuracy of the extension outlined between both techniques was evaluated in the software (GOM Inspect; GOM GmbH) by file superimposition. Specificity and sensitivity tests were applied to measure accuracy. The paired t-test (95% CI) was used to compare the mean total area and the working time. RESULTS. The accuracy ranged from 0.57 to 0.92. The buccal and labial frenulum showed a lower value in the maxilla (0.57); while the area between the retromolar pad and buccal frenulum (0.64) showed a lower score in the mandible. The maxillary denture foundation and the working time for both arches were significantly longer for the digital method (P < .001). CONCLUSION. The denture foundation extension outline exhibited a sufficiently excellent accuracy for the digital method, except for the maxillary anterior region. However, the digital method required a longer working time.