• Journal of Biomedical Engineering Research
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• v.44 no.3
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• pp.211-217
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• 2023

Recently, many studies have introduced artificial intelligence systems in the surgical process to reduce the incidence and mortality of complications in patients. Bleeding is a major cause of operative mortality and complications. However, there have been few studies conducted on detecting bleeding in surgical videos. To advance the development of deep learning models for detecting intraoperative hemorrhage, three models have been trained and compared; such as, YOLOv5, RetinaNet50, and RetinaNet101. We collected 1,016 bleeding images extracted from five surgical videos. The ground truths were labeled based on agreement from two specialists. To train and evaluate models, we divided the datasets into training data, validation data, and test data. For training, 812 images (80%) were selected from the dataset. Another 102 images (10%) were used for evaluation and the remaining 102 images (10%) were used as the evaluation data. The three main metrics used to evaluate performance are precision, recall, and false positive per image (FPPI). Based on the evaluation metrics, RetinaNet101 achieved the best detection results out of the three models (Precision rate of 0.99±0.01, Recall rate of 0.93±0.02, and FPPI of 0.01±0.01). The information on the bleeding detected in surgical videos can be quickly transmitted to the operating room, improving patient 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.10
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• pp.887-895
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• 2013

Metastatic bone cancer, the cancer which occurred in the various organs and progressively spread to bone, is one of the complications in cancer patients. This cancer is divided into the osteoblast and osteolytic metastasis. Although Computer Tomography(CT) could be an useful tool in diagnosis of bone metastasis, lesions are often missed by the visual inspection and it makes clinicians difficult to detect metastasis earlier. Therefore, in this study, we construct a three-dimensional(3D) volume rendering data from tomography images of the chest CT, and apply a 3D based image processing algorithm to them for detection bone metastasis lesions. Then we perform a three-dimensional visualization of the detected lesions.From our test using 10 clinical cases, we confirmed 94.1% of average sensitivity for osteoblast, and 90.0% of average sensitivity, respectively. Consequently, our findings showed a promising possibility and potential usefulness in diagnosis of metastastic bone cancer.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• Journal of the Korean Society of Radiology
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• v.10 no.6
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• pp.443-450
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• 2016

In this study, we are using a computer tomography image of the abdomen, as an experimental linear research for the image of the fatty liver patients texture features analysis and computer-aided diagnosis system of implementation using the ROC curve analysis, from the computer tomography image. We tried to provide an objective and reliable diagnostic information of fatty liver to the doctor. Experiments are usually a fatty liver, via the wavelet transform of the abdominal computed tomography images are configured with the experimental image section, shows the results of statistical analysis on six parameters indicating a feature value of the texture. As a result, the entropy, average luminance, strain rate is shown a relatively high recognition rate of 90% or more, the control also, flatness, uniformity showed relatively low recognition rate of about 70%. ROC curve analysis of six parameters are all shown to 0.900 (p = 0.0001) or more, showed meaningful results in the recognition of the disease. Also, to determine the cut-off value for the prediction of disease six parameters. These results are applicable from future abdominal computed tomography images as a preliminary diagnostic article of diseases automatic detection and eventual diagnosis.

• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.184-191
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• 2015

Prostate ultrasound is used to diagnose prostate cancer, BPH, prostatitis and biopsy of prostate cancer to determine the size of prostate. BPH is one of the common disease in elderly men. Prostate is divided into 4 blocks, peripheral zone, central zone, transition zone, anterior fibromuscular stroma. BPH is histologically transition zone urethra accompanying excessive nodular hyperplasia causes a lower urinary tract symptoms(LUTS) caused by urethral closure as causing the hyperplastic nodule characterized finding progressive ambient. Therefore, in this study normal transition zone image for hyperplasia prostate and normal transition zone image is analyzed quantitatively using a computer algorithm. We applied texture features of GLCM to set normal tissue 60 cases and BPH tissue 60cases setting analysis area $50{\times}50pixels$ which was analyzed by comparing the six parameters for each partial image. Consequently, Disease recognition detection efficiency of Autocorrelation, Cluster prominence, entropy, Sum average, parameter were high as 92~98%.This could be confirmed by quantitative image analysis to nodular hyperplasia change transition zone of the prostate. This is expected secondary means to diagnose BPH and the data base will be considered in various prostate examination.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.4C
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• pp.364-370
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• 2006

It is required for correct signal detection to accurately maintain the synchronization of frequency and timing in a TDMA system. In this paper, we consider nondecision-aided estimation of frequency offset for the transmission of QPSK signal in a TDMA-based satellite system. The proposed scheme estimates the phases of two parts in the burst and then estimates the frequency offset based on the difference between the two estimated phases. Thus, it can provides performance comparable to that of conventional schemes, while significantly reducing the implementation complexity, The performance of the proposed scheme is analyzed and verified by computer simulation, when applied to a GSM based geostationary earth orbit mobile radio(GMR) system.

• 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 the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.3
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• pp.171-176
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• 2013

In this paper, we present a MEMS (micro-electro-mechanical system) implantable blood pressure sensor which has designed and fabricated with consideration of size, design flexibility, and wireless detection. Mechanical and electrical characterizations of the sensor were obtained by mathematical analysis and computer aided simulation. The sensor is composed of two coils and a air gap capacitor formed by separation of the coils. Therefore, the sensor produces its resonant frequency which is changed by external pressure variation. This frequency movement is detected by inductive coupling between the sensor and an external antenna coil. Theoretically analyzed resonant frequency of the sensor under 760 mmHg was calculated to 269.556 MHz. Fused silica was selected as sensor material with consideration of chemical and electrical reaction of human body to the material. $2mm{\times}5mm{\times}0.5mm$ pressure sensors fitted to radial artery were fabricated on the substrates by consecutive microfabrication processes: sputtering, etching, photolithography, direct bonding and laser welding. Resonant frequencies of the fabricated sensors were in the range of 269~284 MHz under 760 mmHg pressure.

• 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.