• Imaging Science in Dentistry
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• v.47 no.4
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• pp.233-239
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• 2017

Purpose: This study was performed to quantify the repeat rate of imaging acquisitions based on different clinical examinations, and to assess the prevalence of error types in intraoral bitewing and periapical imaging using a digital complementary metal-oxide-semiconductor(CMOS) intraoral sensor. Materials and Methods: A total of 8,030 intraoral images were retrospectively collected from 3 groups of undergraduate clinical dental students. The type of examination, stage of the procedure, and reasons for repetition were analysed and recorded. The repeat rate was calculated as the total number of repeated images divided by the total number of examinations. The weighted Cohen's kappa for inter- and intra-observer agreement was used after calibration and prior to image analysis. Results: The overall repeat rate on intraoral periapical images was 34.4%. A total of 1,978 repeated periapical images were from endodontic assessment, which included working length estimation (WLE), trial gutta-percha (tGP), obturation, and removal of gutta-percha (rGP). In the endodontic imaging, the highest repeat rate was from WLE (51.9%) followed by tGP (48.5%), obturation (42.2%), and rGP (35.6%). In bitewing images, the repeat rate was 15.1% and poor angulation was identified as the most common cause of error. A substantial level of intra- and inter-observer agreement was achieved. Conclusion: The repeat rates in this study were relatively high, especially for certain clinical procedures, warranting training in optimization techniques and radiation protection. Repeat analysis should be performed from time to time to enhance quality assurance and hence deliver high-quality health services to patients

• Imaging Science in Dentistry
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• v.44 no.4
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• pp.301-305
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• 2014

Purpose: The purpose of this study was to compare the accuracy of postoperative maxillary cyst (POMC) diagnosis by panoramic radiographs versus computed tomography (CT) and by oral and maxillofacial radiologists versus non-specialists. Materials and Methods: Sixty-five maxillary sinuses with POMCs and 63 without any lesion were assessed using panoramic radiographs and CT images by five oral and maxillofacial radiologists and five non-specialists on a five-point scale. The areas under receiver operating characteristic (ROC) curves were analyzed to determine the differences in diagnostic accuracy between the two imaging modalities and between the two groups of observers. The intra-observer agreement was determined, too. Results: The diagnostic accuracy of CT images was higher than that of panoramic radiographs in both groups of observers (p<0.05). The diagnostic accuracy of oral and maxillofacial radiologists for each method was higher than that of non-specialists (p<0.05). Conclusion: The use of CT improves the diagnosis of POMC, and radiological training and experience leads to more accurate evaluation.

• Journal of radiological science and technology
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• v.23 no.1
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• pp.63-76
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• 2000

License system of radiologic technologists has been started since 1965 in Korea. This study is to explore directions on radiotechnologists' license system classified by subspecialty. For this purpose, the authors surveyed on radiotechnologists' license system classified by subspecialty, with the subject related to radiotechnologic societies. Additionally, data on qualification and license system associated with medical and health care field were collected. The results are as follows. 1. The main body for subspecialty system for radiologic technologists should be the Korea Radiologic Technologists Association and the Association should maintain a close cooperation with radiotechnologic societies. 2. A radiologic technologist should be a basic role once they pass the license examination. In addition, they can get a special qualification by subspecialty in radiologic technology. 3. Radiotechnologists' license system classified by subspecialty will be keep priorities in order and done systematically. Execution order is as follows ; This study proposes that radiotechnologists responsible for ultrasonography, computed tomography(CT), magnetic resonance imaging(MRI) and security management be started for the first stage. For the second stage, radiotechnologists for mammography, angio-cardiography, digital imaging, maxillo-facial and dental radiography, nuclear medicine, radio-therapeutic field should be in force. 4. Professional education course(basic and intensive) and clinical training program have to be made for the eligibility of radiotechnologists' license system classified by subspecialty. 5. Eligibility system of radiotechnologists' license system classified by subspecialty(non-government or government) has to be made. Further more, inquiry commission to investigate eligibility for radiotechnologists' license system should be established.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.383-391
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• 2022

Purpose: Periodontitis, the most prevalent chronic inflammatory condition affecting teeth-supporting tissues, is diagnosed and classified through clinical and radiographic examinations. The staging of periodontitis using panoramic radiographs provides information for designing computer-assisted diagnostic systems. Performing image segmentation in periodontitis is required for image processing in diagnostic applications. This study evaluated image segmentation for periodontitis staging based on deep learning approaches. Materials and Methods: Multi-Label U-Net and Mask R-CNN models were compared for image segmentation to detect periodontitis using 100 digital panoramic radiographs. Normal conditions and 4 stages of periodontitis were annotated on these panoramic radiographs. A total of 1100 original and augmented images were then randomly divided into a training (75%) dataset to produce segmentation models and a testing (25%) dataset to determine the evaluation metrics of the segmentation models. Results: The performance of the segmentation models against the radiographic diagnosis of periodontitis conducted by a dentist was described by evaluation metrics(i.e., dice coefficient and intersection-over-union [IoU] score). MultiLabel U-Net achieved a dice coefficient of 0.96 and an IoU score of 0.97. Meanwhile, Mask R-CNN attained a dice coefficient of 0.87 and an IoU score of 0.74. U-Net showed the characteristic of semantic segmentation, and Mask R-CNN performed instance segmentation with accuracy, precision, recall, and F1-score values of 95%, 85.6%, 88.2%, and 86.6%, respectively. Conclusion: Multi-Label U-Net produced superior image segmentation to that of Mask R-CNN. The authors recommend integrating it with other techniques to develop hybrid models for automatic periodontitis detection.

• Imaging Science in Dentistry
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• v.52 no.4
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• pp.351-357
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• 2022

Purpose: Convolutional neural networks (CNNs) have rapidly emerged as one of the most promising artificial intelligence methods in the field of medical and dental research. CNNs can provide an effective diagnostic methodology allowing for the detection of early-staged diseases. Therefore, this study aimed to evaluate the performance of a deep CNN algorithm for apical lesion segmentation from panoramic radiographs. Materials and Methods: A total of 1000 panoramic images showing apical lesions were separated into training (n=800, 80%), validation (n=100, 10%), and test (n=100, 10%) datasets. The performance of identifying apical lesions was evaluated by calculating the precision, recall, and F1-score. Results: In the test group of 180 apical lesions, 147 lesions were segmented from panoramic radiographs with an intersection over union (IoU) threshold of 0.3. The F1-score values, as a measure of performance, were 0.828, 0.815, and 0.742, respectively, with IoU thresholds of 0.3, 0.4, and 0.5. Conclusion: This study showed the potential utility of a deep learning-guided approach for the segmentation of apical lesions. The deep CNN algorithm using U-Net demonstrated considerably high performance in detecting apical lesions.

• 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.

• Journal of Veterinary Clinics
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• v.40 no.3
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• pp.215-220
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• 2023

A 3-month-old Korean native cattle (Hanwoo) calf with difficulty taking normal posture and an inability to rise was referred for a definite diagnosis and active treatment, including surgery. The calf had a history of an accident in which both hind limbs were trapped in a barn structure. After admission, a "rabbit leg" posture was observed, a typical sign of gastrocnemius muscle rupture, and both digits were knuckled downward like they were trying to grip the ground. This was considered to be a result of the superficial digital flexor not rupturing but only the gastrocnemius muscle rupturing. Physical examination revealed laceration of the metatarsus and firmness behind both stifle joints which were presumed to be the sites of gastrocnemius muscle rupture. Skeletal abnormalities, including fractures, were ruled out by radiography. Based on these findings, the patient was diagnosed with bilateral gastrocnemius muscle rupture, and surgery was performed to reconnect the head of the ruptured muscle. Because the rupture occurred perpendicular to the muscle direction, the locking loop technique, a method of suturing severed tendons, was used to reduce the tension. After surgery, the cast was used to prevent further injuries and promote voluntary rehabilitation. Follow-up was completed, with the calf showing normal posture and gait 112 days after surgery. This is the first case report in the Republic of Korea describing the successful diagnosis and treatment of bilateral gastrocnemius muscle rupture in a calf.

• Journal of dental hygiene science
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• v.23 no.4
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• pp.343-350
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• 2023

Background: Digital panoramic dental X-ray equipment (PDX) is frequently used by patients and dental workers for diagnosis and examination in dental institutions; however, infection control has not been properly implemented. Therefore, in this study, we aimed to systematically review the potential risk of cross-infection in the dental environment by investigating the contamination level of general aerobic bacteria and Staphylococcus aureus, which are important in hospital infections, in PDX areas that people mainly contact. Methods: This survey was conducted from March to May 2023 and covered one general hospital, three dental hospitals, and nine dental clinics equipped with PDX. Bacteria samples were collected from the left-handle, right-handle, forehead support, and head side support as the patient's contact areas, as well as the X-ray exposure switch and left-click mouse button as the dental hygienist's contact areas of the PDX. The collected bacteria were spread on Petrifilm, and colonies formed after 48 hours of culture were counted. Results: General aerobic bacteria and S. aureus were detected in all areas investigated. Significant differences in bacterial counts between different regions of the PDX were observed in both groups (p<0.001). The detection rates of general aerobic bacteria (p<0.001) and S. aureus (p<0.001) were significantly higher in the contact areas of patients than those of dental hygienists. A positive correlation was observed between the forehead and the temple region in terms of general aerobic bacteria and S. aureus detection (r=1) (p<0.01). Conclusion: Taken together, the presence of many bacteria, including S. aureus, detected in PDX indicates that PDX has a potential cross-infection risk. Our results therefore highlight the need for the development of appropriate disinfection protocols for reusable medical devices such as PDX and periodic infection prevention training for hospital-related workers, including dental hygienists.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.53-63
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• 2024

Alzheimer's disease is a brain disorder that worsens over time and affects millions of people around the world. It leads to a gradual deterioration in memory, thinking ability, and behavioral and social skills until the person loses his ability to adapt to society. Technological progress in medical imaging and the use of artificial intelligence, has provided the possibility of detecting Alzheimer's disease through medical images such as magnetic resonance imaging (MRI). However, Deep learning algorithms, especially convolutional neural networks (CNNs), have shown great success in analyzing medical images for disease diagnosis and classification. Where CNNs can recognize patterns and objects from images, which makes them ideally suited for this study. In this paper, we proposed to compare the performances of Alzheimer's disease detection by using two deep learning methods: You Only Look Once (YOLO), a CNN-enabled object recognition algorithm, and Visual Geometry Group (VGG16) which is a type of deep convolutional neural network primarily used for image classification. We will compare our results using these modern models Instead of using CNN only like the previous research. In addition, the results showed different levels of accuracy for the various versions of YOLO and the VGG16 model. YOLO v5 reached 56.4% accuracy at 50 epochs and 61.5% accuracy at 100 epochs. YOLO v8, which is for classification, reached 84% accuracy overall at 100 epochs. YOLO v9, which is for object detection overall accuracy of 84.6%. The VGG16 model reached 99% accuracy for training after 25 epochs but only 78% accuracy for testing. Hence, the best model overall is YOLO v9, with the highest overall accuracy of 86.1%.