• The Journal of Korean Academy of Prosthodontics
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• v.55 no.1
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• pp.79-87
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• 2017

The open bite malocclusion is a common clinical entity and has multifactorial causes. Development of effective treatment plan and management is dependent on proper diagnosis. The skeletal open bite patient requires a coordinated orthodontic and orthognathic surgical approach to achieve stable occlusion, acceptable esthetics, and improved function. But in case of open bite with severely decayed dentition, restoration in the entire dentition is necessary. Using the facial analysis and diagnostic wax-up, the most effective treatment was prosthetic rehabilitation. The provisional restorations were fabricated to satisfy esthetic and functional requirements, which result in the uniformly distributed occlusal force, anterior and canine guidance. The inter-arch relationship, labio-dental harmony, and the soft tissue aspect, which is important to estimate the longevity were evaluated. Definitive restorations of monolithic zirconia were made by replicating provisional restorations by using the latest CAD/CAM technology. They were delivered to the patient and clinical follow-up observation was satisfactory.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2482-2484
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• 2000

In this paper, we have developed an automated computer aided diagnostic (CAD) scheme by using artificial neural networks(ANN) on guantitative analysis of chest photofluorography. The first ANN performs the detection of suspicious regions in a low resolution image. This was trained specifically on the problem of detecting abnormal regions digitized chest photofluorography. The second space matching method was used to distinguish between normal and abnormal regions of interest(ROI). If the ratio of the number of abnormal ROI to the total number of all ROI in a chest image was greater than a specified threshold level, the image was classified as abnormal.

• Journal of the Korean Society of Radiology
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• v.7 no.1
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• pp.9-15
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• 2013

In this study, texture feature analysis (TFA) algorithm to automatic recognition of liver disease suggests by utilizing computed tomography (CT), by applying the algorithm computer-aided diagnosis (CAD) of hepatocellular carcinoma (HCC) design. Proposed the performance of each algorithm was to comparison and evaluation. In the HCC image, set up region of analysis (ROA, window size was $40{\times}40$ pixels) and by calculating the figures for TFA algorithm of the six parameters (average gray level, average contrast, measure of smoothness, skewness, measure of uniformity, entropy) HCC recognition rate were calculated. As a result, TFA was found to be significant as a measure of HCC recognition rate. Measure of uniformity was the most recognition. Average contrast, measure of smoothness, and skewness were relatively high, and average gray level, entropy showed a relatively low recognition rate of the parameters. In this regard, showed high recognition algorithms (a maximum of 97.14%, a minimum of 82.86%) use the determining HCC imaging lesions and assist early diagnosis of clinic. If this use to therapy, the diagnostic efficiency of clinical early diagnosis better than before. Later, after add the effective and quantitative analysis, criteria research for generalized of disease recognition is needed to be considered.

• The Journal of Korean Academy of Prosthodontics
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• v.60 no.4
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• pp.431-441
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• 2022

The digital facebow device records the trajectory of the mandibular movement where it is then reflected on the computer-aided design software, leading to an improvement on treatment outcomes of prosthetic restorations. In this clinical case, using a digital technology, an implant placement and prosthetic restoration were done in a patient who has lost maxillary and mandibular molars. Following an intraoral scan, a surgical stent for implant surgery was fabricated based on digital diagnostic wax-up, and implants were installed. After six months of sufficient osseointegration, customized abutments and the first temporary prostheses were delivered. Then two months later, at an abutment level, an intraoral scan and digital facebow transfer device were used to mount the intraoral scan data on a virtual articulator, and record the mandibular movements. Once the second temporary prostheses were fabricated and delivered on a basis of the mandibular movement, the definitive zirconia prostheses were designed and delivered based on a stabilized occlusion that was duplicated via double scan technique.

• Imaging Science in Dentistry
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• v.43 no.3
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• pp.153-161
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• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• 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.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.

• The Journal of Korean Academy of Prosthodontics
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• v.61 no.3
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• pp.215-226
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• 2023

Severe wear of the anterior teeth facilitates the loss of anterior guidance, which protects the posterior teeth from wear during excursive movement. Additionally, when treating patients with collapsed occlusion due to multiple tooth loss and tooth wear, it is important to determine the presence of vertical dimension loss through accurate clinical and radiographic examinations and diagnostic wax-up. The patient of this case is a 44-year-old female patient who complained of overall tooth wear and loss of posterior teeth due to bruxism and clenching habits, visited the hospital with the address of restoring masticatory function and improving aesthetic appearance through prosthetic treatment. Through model analysis and diagnostic wax-up, an increase in vertical dimension was determined, and full mouth restoration with fixed prostheses was planned. The degree of adaptation to the vertical dimension was confirmed step by step using an occlusal splint designed with CAD (Computer aided design) software and 3-D (3-Dimensional) printed, and then restored with provisional restoration and after a 4-month adaptation period, the entire dentition was restored with metal ceramic crowns and implants. Through this procedure, satisfactory treatment results were obtained in terms of function and aesthetics.

• 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.