• Imaging Science in Dentistry
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• v.52 no.3
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• pp.283-288
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• 2022

Purpose: This study was conducted to measure the radiation exposure and image quality of various cone-beam computed tomography (CBCT) machines under common clinical conditions and to analyze the correlation between them. Materials and Methods: Seven CBCT machines used frequently in clinical practice were selected. Because each machine has various sizes of fields of view (FOVs), 1 large FOV and 1 small FOV were selected for each machine. Radiation exposure was measured using a dose-area product (DAP) meter. The quality of the CBCT images was analyzed using 8 image quality parameters obtained using a dental volume tomography phantom. For statistical analysis, regression analysis using a generalized linear model was used. Results: Polymethyl-methacrylate (PMMA) noise and modulation transfer function (MTF) 10% showed statistically significant correlations with DAP values, presenting positive and negative correlations, respectively (P<0.05). Image quality parameters other than PMMA noise and MTF 10% did not demonstrate statistically significant correlations with DAP values. Conclusion: As radiation exposure and image quality are not proportionally related in clinically used equipment, it is necessary to evaluate and monitor radiation exposure and image quality separately.

• Journal of Trauma and Injury
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• v.35 no.1
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• pp.25-33
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• 2022

Purpose: The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. This study aimed to validate the SSTLI as a prognostic factor in patients with polytrauma and liver injury through a generalized estimating equation analysis. Methods: The medical records of 521 patients with traumatic liver injury from January 2015 to December 2019 were reviewed. The primary outcome variable was in-hospital mortality. All the risk factors were analyzed using multivariate logistic regression analysis. The SSTLI has five clinical measures (age, Injury Severity Score, serum total bilirubin level, prothrombin time, and creatinine level) chosen based on their predictive power. Each measure is scored as 0-1 (age and Injury Severity Score) or 0-3 (serum total bilirubin level, prothrombin time, and creatinine level). The SSTLI score corresponds to the total points for each item (0-11 points). Results: The areas under the curve of the SSTLI to predict mortality on post-traumatic days 0, 1, 3, and 5 were 0.736, 0.783, 0.830, and 0.824, respectively. A very good to excellent positive correlation was observed between the probability of mortality and the SSTLI score (γ=0.997, P<0.001). A value of 5 points was used as the threshold to distinguish low-risk (<5) from high-risk (≥5) patients. Multivariate analysis using the generalized estimating equation in the logistic regression model indicated that the SSTLI score was an independent predictor of mortality (odds ratio, 1.027; 95% confidence interval, 1.018-1.036; P<0.001). Conclusions: The SSTLI was verified to predict mortality in patients with polytrauma and liver injury. A score of ≥5 on the SSTLI indicated a high-risk of post-traumatic mortality.

• Journal of radiological science and technology
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• v.46 no.4
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• pp.325-336
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• 2023

The purpose of this study was to analyze the structural relationship between job satisfaction levels felt during ultrasound examination of radiological technologists (RTs) using a structural equation model. The subjects were a total of 203 RTs currently working in medical institutions. The method was conducted as a survey study using a questionnaire consisting of a total of 29 questions consisting of demographic characteristics and job satisfaction levels that were self-efficacy, job competency, extrinsic compensation, and job satisfaction. The reliability was secured with the Cronbach's alpha coefficient of 0.6 or higher. For statistical analysis, a significant difference between the frequency analysis of demographic characteristics and the mean of the job satisfaction levels were performed by independent sample T-test and one-way analysis of variance (ANOVA) followed by Scheffe's post hoc test. The correlation analysis between variables was tested with Spearman's and Pearson's correlation coefficient. We analyzed the structural relationships between variables by structural equations. As a result, first, job competency and extrinsic compensation had a positive effect on job satisfaction on ultrasound examination of RTs. Second, the self-efficacy of ultrasound examination RTs showed a high correlation with job competency. Third, the job satisfaction levels showed in the order of job competency, job satisfaction, self-efficacy, and extrinsic compensation. In conclusion, this study are expected to be provided as data to identify factors that could improve job satisfaction during ultrasound examination of RTs by empirically analyzing the structural relationship of self-efficacy, job competency, and external compensation.

• Clinical and Experimental Reproductive Medicine
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• v.50 no.3
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• pp.200-205
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• 2023

Objective: Polycystic ovary (PCO), a diagnostic component of polycystic ovary syndrome (PCOS), requires either an ovarian volume (OV) criterion or a follicle number per ovary (FNPO) criterion. This study investigated the association of OV and FNPO criteria with various manifestations of PCOS. Methods: This cross-sectional study was conducted at a university hospital among 100 patients newly diagnosed with PCOS (according to the revised Rotterdam criteria). Fasting blood samples were collected to measure glucose, total testosterone (TT), luteinizing hormone (LH), follicle-stimulating hormone (FSH), lipid, insulin, and hemoglobin A1c levels. An oral glucose tolerance test was performed. Transabdominal or transvaginal ultrasound of the ovaries was done, depending on patients' marital status. All investigations were conducted in the follicular phase of the menstrual cycle. OV >10 mL and/or FNPO ≥12 indicated PCO. A homeostasis model assessment of insulin resistance (IR) value ≥2.6 indicated IR, and metabolic syndrome (MS) was defined according to the international harmonization criteria. Results: Seventy-six participants fulfilled the OV criterion, 70 fulfilled the FNPO criterion, and 89 overall had PCO. Both maximum OV and mean OV had a significant correlation with TT levels (r=0.239, p=0.017 and r=0.280, p=0.005, respectively) and the LH/FSH ratio (r=0.212, p=0.034 and r=0.200, p=0.047, respectively). Mean OV also had a significant correlation with fasting insulin levels (r=0.210, p=0.036). Multivariate binary logistic regression analysis showed that IR (odds ratio [OR], 9.429; 95% confidence interval [CI], 1.701 to 52.271; p=0.010) and MS (OR, 7.952; 95% CI, 1.821 to 34.731; p=0.006) had significant predictive associations with OV alone, even after adjustment for age and body mass index. Conclusion: OV may be more closely related to the androgenic and metabolic characteristics of PCOS than FNPO.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.279-282
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• 2022

Acute lymphoblastic leukemia is an acute leukemia caused by suppression of bone marrow function due to overgrowth of immature lymphocytes in the bone marrow. It accounts for 30% of acute leukemia in adults, and children show a cure rate of over 80% with chemotherapy, while adults show a low survival rate of 20% to 50%. However, research on a machine learning algorithm based on medical image data for the diagnosis of acute lymphoblastic leukemia is in the initial stage. In this paper, we compare and analyze CNN algorithm models for quick and accurate diagnosis. Using four models, an experimental environment for comparative analysis of acute lymphoblastic leukemia diagnostic models was established, and the algorithm with the best accuracy was selected for the given medical image data. According to the experimental results, among the four CNN models, the InceptionV3 model showed the best performance with an accuracy of 98.9%.

• Journal of Periodontal and Implant Science
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• v.53 no.1
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• pp.38-53
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• 2023

Purpose: The current Classification of Periodontal and Peri-Implant Diseases and Conditions, published and disseminated in 2018, involves some difficulties and causes diagnostic conflicts due to its criteria, especially for inexperienced clinicians. The aim of this study was to design a decision system based on machine learning algorithms by using clinical measurements and radiographic images in order to determine and facilitate the staging and grading of periodontitis. Methods: In the first part of this study, machine learning models were created using the Python programming language based on clinical data from 144 individuals who presented to the Department of Periodontology, Faculty of Dentistry, Süleyman Demirel University. In the second part, panoramic radiographic images were processed and classification was carried out with deep learning algorithms. Results: Using clinical data, the accuracy of staging with the tree algorithm reached 97.2%, while the random forest and k-nearest neighbor algorithms reached 98.6% accuracy. The best staging accuracy for processing panoramic radiographic images was provided by a hybrid network model algorithm combining the proposed ResNet50 architecture and the support vector machine algorithm. For this, the images were preprocessed, and high success was obtained, with a classification accuracy of 88.2% for staging. However, in general, it was observed that the radiographic images provided a low level of success, in terms of accuracy, for modeling the grading of periodontitis. Conclusions: The machine learning-based decision system presented herein can facilitate periodontal diagnoses despite its current limitations. Further studies are planned to optimize the algorithm and improve the results.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.459-464
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• 2023

Due to the development of advanced technology, a lot of digital radiographic equipment has been developed, which is very helpful for accurate diagnosis and treatment, and it is very important to train personnel who have acquired professional knowledge in order to use it safely and effectively. Students are exposed to the risk of radiation exposure in radiography training using diagnostic X-ray equipment, and some educational institutions do not use X-ray equipment due to management difficulties in accordance with the Nuclear Safety Act. As a solution to this, this study developed a medical radiation simulator for education that does not generate radiation by using a vision sensor and self-developed software. Through this, educational institutions can reduce the burden of administrative implementation according to the law, and students can obtain a high level of educational effects in a healthy practice environment without radiation exposure.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.292-294
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• 2021

The digital twin-based smart pipe integrated management system is an integrated solution for efficient operation and monitoring that we propose. We buried a waterway pipe underground with self-diagnostic and condition monitoring sensor functions. This pipe sends sensing data and accumulates it. Our system analyzes data to make smart decisions. The main functions of this system are remote control and monitoring. Therefore, "how to configure monitoring in real time" is a big issue. For this purpose, we designed a special real-time-based agent function. In this paper, to solve this problem, a layered architecture was proposed based on transmission points where sensor data are exchanged. An agent was placed in each layer to look at the lower layer and periodically monitor whether there were any changes in the sensor in real time. Finally, the agent system was designed and the conceptual model level was implemented to verify excellence.

• Anatomy and Cell Biology
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• v.56 no.1
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• pp.86-93
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• 2023

Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.

• Journal of Microbiology and Biotechnology
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• v.33 no.11
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• pp.1506-1512
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• 2023

Quantitative analysis of adenosine triphosphate (ATP) has been widely used as a diagnostic tool in the food and medical industries. Particularly, the pathogenesis of a few diseases including inflammatory bowel disease (IBD) is closely related to high ATP concentrations. A bioluminescent D-luciferin/luciferase system, which includes a luciferase (FLuc) from the firefly Photinus pyralis as a key component, is the most commonly used method for the detection and quantification of ATP. Here, instead of isolating FLuc produced in recombinant Escherichia coli, we aimed to develop a whole-cell biocatalyst system that does not require extraction and purification of FLuc. To this end, the gene coding for FLuc was introduced into the genome of probiotic Saccharomyces boulardii using the CRISPR/Cas9-based genome editing system. The linear relationship (r² = 0.9561) between ATP levels and bioluminescence generated from the engineered S. boulardii expressing FLuc was observed in vitro. To explore the feasibility of using the engineered S. boulardii expressing FLuc as a whole-cell biosensor to detect inflammation biomarker (i.e., ATP) in the gut, a colitis mouse model was established using dextran sodium sulfate as a colitogenic compound. Our findings demonstrated that the whole-cell biosensor can detect elevated ATP levels during gut inflammation in mice. Therefore, the simple and powerful method developed herein could be applied for non-invasive IBD diagnosis.