1.A Study on Reproducible Locations for Evaluating Masseter Muscle Function with Ultrasonography
Hyun-Jeong PARK ; Jong-Mo AHN ; Sun-Kyoung YU ; Ji-Won RYU
Journal of Oral Medicine and Pain 2025;50(1):25-33
Purpose:
This study aimed to identify reproducible locations for evaluating masseter muscle function by measuring its thickness using ultrasonography (US). The study focused on comparing two measurement locations: the thickest part of the masseter muscle during ultrasonographic scanning (TMUS) and the most prominent part during clenching (PMC).
Methods:
Forty healthy adults (20 males and 20 females) participated in the study. US images were obtained from both sides of the masseter muscle under resting and clenching conditions. Measurements were taken at the TMUS and PMC locations, and the clenching-to-resting (C/R) ratio was calculated. Intra- and inter-rater reliability were assessed using intraclass correlation coefficients (ICCs), and the agreement between the two locations was further analyzed using Bland–Altman (BA) plots.
Results:
The measurements at both TMUS and PMC showed high intra- and inter-rater agreement, with no significant difference in measurements between the two locations.However, the PMC location demonstrated slightly higher ICC values (0.94) compared to TMUS (0.91). The C/R ratio for PMC showed higher consistency (0.89) compared to TMUS (0.65). BA plots indicated that the agreement between TMUS and PMC was slightly better during clenching than at rest, with smaller mean differences in clenching (–0.06 mm) than resting (–0.13 mm). Additionally, the number of measurements outside the upper and lower limits was lower during clenching (10) than at rest (13).
Conclusions
Both TMUS and PMC locations demonstrated reliable measurements, but the PMC location showed slightly better consistency across different muscle states. The findings suggest that PMC provides a more reproducible and standardized approach for masseter muscle assessment, making it a better choice for both clinical practice and research in evaluating masticatory function.
2.Dental Age Estimation in Children Using Convolution Neural Network Algorithm: A Pilot Study
Byung-Yoon ROH ; Hyun-Jeong PARK ; Kyung-Ryoul KIM ; In-Soo SEO ; Yeon-Ho OH ; Ju-Heon LEE ; Chang-Un CHOI ; Yo-Seob SEO ; Ji-Won RYU ; Jong-Mo AHN
Journal of Oral Medicine and Pain 2024;49(4):118-123
Purpose:
Recently, deep learning techniques have been introduced for age estimation, with automated methods based on radiographic analysis demonstrating high accuracy. In this study, we applied convolutional neural network (CNN) techniques to the lower dentition area on orthopantomograms (OPGs) of children to develop an automated age estimation model and evaluate its accuracy for use in forensic dentistry.
Methods:
In this study, OPGs of 2,856 subjects aged 3-14 years were analyzed. The You Only Look Once (YOLO) V8 object detection technique was applied to extract the mandibular dentition area on OPGs, designating it as the region of interest (ROI). First, 200 radiographs were randomly selected, and were used to train a model for extracting the ROI. The trained model was then applied to the entire dataset. For the CNN image classification task, 80% of OPGs were allocated to the training set, while the remaining 20% were used as the test set. A transfer learning approach was employed using the ResNet50 and VGG19 backbone models, with an ensemble technique combining these models to improve performance. The mean absolute error (MAE) on the test set was used as the validation metric, and the model with the lowest MAE was selected.
Results:
In this study, the age estimation model developed using mandibular dentition region from OPGs achieved MAE and root mean squared error (RMSE) values of 0.501 and 0.742, respectively, on the test set, and MAE and RMSE values of 0.273 and 0.354, respectively, on the training set.
Conclusions
The automated age estimation model developed in this study demonstrated accuracy comparable to that of previous research and shows potential for applications in forensic investigations. Increasing the sample size and incorporating diverse deep learning techniques are expected to further enhance the accuracy of future age estimation models.
3.Orofacial Pain and Nonodotogenic Toothache of Cardiac Origin:Case Report
Jong-Mo AHN ; Ji-Won RYU ; Hyun-Jeong PARK
Journal of Oral Medicine and Pain 2024;49(1):18-21
Orofacial pain has various causes, making it challenging to differentiate from dentalrelated diseases based solely on symptoms. Toothache, usually attributed to pathological changes in the pulp and periodontal tissue, is the most common cause of orofacial pain and relatively easy to diagnose. However, distinguishing orofacial pain and nonodontogenic toothache due to myofascial, neuropathic, neurovascular, paranasal sinus and cardiac originating, and psychogenic pain presents diagnostic challenges that may result in incorrect treatment. Therefore, dentists must recognize that orofacial pain can arise from not only dental issues but also other causes. This case report explores the necessary considerations in diagnosing orofacial pain and nonodontogenic toothache by examining the diagnoses of patients presenting at the dental hospital with orofacial pain and nonodontogenic toothache of cardiac origin.
4.Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea
Byung-Joon KIM ; Jun-Seop SHIN ; Byoung-Hoon MIN ; Jong-Min KIM ; Chung-Gyu PARK ; Hee-Jung KANG ; Eung Soo HWANG ; Won-Woo LEE ; Jung-Sik KIM ; Hyun Je KIM ; Iov KWON ; Jae Sung KIM ; Geun Soo KIM ; Joonho MOON ; Du Yeon SHIN ; Bumrae CHO ; Heung-Mo YANG ; Sung Joo KIM ; Kwang-Won KIM
Diabetes & Metabolism Journal 2024;48(6):1160-1168
Background:
Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans.
Methods:
A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness.
Conclusion
A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.
5.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
6.Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea
Byung-Joon KIM ; Jun-Seop SHIN ; Byoung-Hoon MIN ; Jong-Min KIM ; Chung-Gyu PARK ; Hee-Jung KANG ; Eung Soo HWANG ; Won-Woo LEE ; Jung-Sik KIM ; Hyun Je KIM ; Iov KWON ; Jae Sung KIM ; Geun Soo KIM ; Joonho MOON ; Du Yeon SHIN ; Bumrae CHO ; Heung-Mo YANG ; Sung Joo KIM ; Kwang-Won KIM
Diabetes & Metabolism Journal 2024;48(6):1160-1168
Background:
Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans.
Methods:
A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness.
Conclusion
A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.
7.Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea
Byung-Joon KIM ; Jun-Seop SHIN ; Byoung-Hoon MIN ; Jong-Min KIM ; Chung-Gyu PARK ; Hee-Jung KANG ; Eung Soo HWANG ; Won-Woo LEE ; Jung-Sik KIM ; Hyun Je KIM ; Iov KWON ; Jae Sung KIM ; Geun Soo KIM ; Joonho MOON ; Du Yeon SHIN ; Bumrae CHO ; Heung-Mo YANG ; Sung Joo KIM ; Kwang-Won KIM
Diabetes & Metabolism Journal 2024;48(6):1160-1168
Background:
Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans.
Methods:
A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness.
Conclusion
A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.
8.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
9.Occupation classification model based on DistilKoBERT: using the 5th and 6th Korean Working Condition Surveys
Tae-Yeon KIM ; Seong-Uk BAEK ; Myeong-Hun LIM ; Byungyoon YUN ; Domyung PAEK ; Kyung Ehi ZOH ; Kanwoo YOUN ; Yun Keun LEE ; Yangho KIM ; Jungwon KIM ; Eunsuk CHOI ; Mo-Yeol KANG ; YoonHo CHO ; Kyung-Eun LEE ; Juho SIM ; Juyeon OH ; Heejoo PARK ; Jian LEE ; Jong-Uk WON ; Yu-Min LEE ; Jin-Ha YOON
Annals of Occupational and Environmental Medicine 2024;36(1):e19-
Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT. This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics. The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset. This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.
10.Clinical Trial Protocol for Porcine Islet Xenotransplantation in South Korea
Byung-Joon KIM ; Jun-Seop SHIN ; Byoung-Hoon MIN ; Jong-Min KIM ; Chung-Gyu PARK ; Hee-Jung KANG ; Eung Soo HWANG ; Won-Woo LEE ; Jung-Sik KIM ; Hyun Je KIM ; Iov KWON ; Jae Sung KIM ; Geun Soo KIM ; Joonho MOON ; Du Yeon SHIN ; Bumrae CHO ; Heung-Mo YANG ; Sung Joo KIM ; Kwang-Won KIM
Diabetes & Metabolism Journal 2024;48(6):1160-1168
Background:
Islet transplantation holds promise for treating selected type 1 diabetes mellitus patients, yet the scarcity of human donor organs impedes widespread adoption. Porcine islets, deemed a viable alternative, recently demonstrated successful longterm survival without zoonotic risks in a clinically relevant pig-to-non-human primate islet transplantation model. This success prompted the development of a clinical trial protocol for porcine islet xenotransplantation in humans.
Methods:
A single-center, open-label clinical trial initiated by the sponsor will assess the safety and efficacy of porcine islet transplantation for diabetes patients at Gachon Hospital. The protocol received approval from the Gachon Hospital Institutional Review Board (IRB) and the Korean Ministry of Food and Drug Safety (MFDS) under the Investigational New Drug (IND) process. Two diabetic patients, experiencing inadequate glycemic control despite intensive insulin treatment and frequent hypoglycemic unawareness, will be enrolled. Participants and their family members will engage in deliberation before xenotransplantation during the screening period. Each patient will receive islets isolated from designated pathogen-free pigs. Immunosuppressants and systemic infection prophylaxis will follow the program schedule. The primary endpoint is to confirm the safety of porcine islets in patients, and the secondary endpoint is to assess whether porcine islets can reduce insulin dose and the frequency of hypoglycemic unawareness.
Conclusion
A clinical trial protocol adhering to global consensus guidelines for porcine islet xenotransplantation is presented, facilitating streamlined implementation of comparable human trials worldwide.

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