Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objectives: This study aimed to investigate the current status of periodontal disease programs implemented by public health centers in the Republic of Korea. Methods: An explanatory survey was conducted by the Ministry of Health and Welfare from October to November 2023. The survey focused on the periodontal programs and the implementation status across different stages. Distributed and collected via Google Forms, the survey targeted 196 oral health teams within public health centers in Korea. A total of 109 public health centers responded to the study questionnaire, yielding a participation rate of 55.6%. Data were analyzed using IBM SPSS Statistics for Windows, version 26. Results: A majority of periodontal disease programs were implemented exclusively by oral health teams, with a rate of 33.0%. The implementation rate of collaboration with home-visiting health teams was 17.4% and with other teams was 10.1%. The implementation rates of periodontal management across stages were as follows: 11.9% for periodontal examination, 18.3% for periodontal treatment, and 11.9% for sustainable periodontal care. Conclusions Periodontal disease programs are predominantly conducted by oral health teams with limited collaboration across other health teams. Additionally, periodontal management activities, such as examinations and treatments, remain insufficient. Integration between oral health teams and other health teams within public health centers or private dental clinics should be improved.

Objectives: This study aimed to investigate the current status of periodontal disease programs implemented by public health centers in the Republic of Korea. Methods: An explanatory survey was conducted by the Ministry of Health and Welfare from October to November 2023. The survey focused on the periodontal programs and the implementation status across different stages. Distributed and collected via Google Forms, the survey targeted 196 oral health teams within public health centers in Korea. A total of 109 public health centers responded to the study questionnaire, yielding a participation rate of 55.6%. Data were analyzed using IBM SPSS Statistics for Windows, version 26. Results: A majority of periodontal disease programs were implemented exclusively by oral health teams, with a rate of 33.0%. The implementation rate of collaboration with home-visiting health teams was 17.4% and with other teams was 10.1%. The implementation rates of periodontal management across stages were as follows: 11.9% for periodontal examination, 18.3% for periodontal treatment, and 11.9% for sustainable periodontal care. Conclusions Periodontal disease programs are predominantly conducted by oral health teams with limited collaboration across other health teams. Additionally, periodontal management activities, such as examinations and treatments, remain insufficient. Integration between oral health teams and other health teams within public health centers or private dental clinics should be improved.

Objectives: This study aimed to investigate the current status of periodontal disease programs implemented by public health centers in the Republic of Korea. Methods: An explanatory survey was conducted by the Ministry of Health and Welfare from October to November 2023. The survey focused on the periodontal programs and the implementation status across different stages. Distributed and collected via Google Forms, the survey targeted 196 oral health teams within public health centers in Korea. A total of 109 public health centers responded to the study questionnaire, yielding a participation rate of 55.6%. Data were analyzed using IBM SPSS Statistics for Windows, version 26. Results: A majority of periodontal disease programs were implemented exclusively by oral health teams, with a rate of 33.0%. The implementation rate of collaboration with home-visiting health teams was 17.4% and with other teams was 10.1%. The implementation rates of periodontal management across stages were as follows: 11.9% for periodontal examination, 18.3% for periodontal treatment, and 11.9% for sustainable periodontal care. Conclusions Periodontal disease programs are predominantly conducted by oral health teams with limited collaboration across other health teams. Additionally, periodontal management activities, such as examinations and treatments, remain insufficient. Integration between oral health teams and other health teams within public health centers or private dental clinics should be improved.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Objectives: This study aimed to investigate the current status of periodontal disease programs implemented by public health centers in the Republic of Korea. Methods: An explanatory survey was conducted by the Ministry of Health and Welfare from October to November 2023. The survey focused on the periodontal programs and the implementation status across different stages. Distributed and collected via Google Forms, the survey targeted 196 oral health teams within public health centers in Korea. A total of 109 public health centers responded to the study questionnaire, yielding a participation rate of 55.6%. Data were analyzed using IBM SPSS Statistics for Windows, version 26. Results: A majority of periodontal disease programs were implemented exclusively by oral health teams, with a rate of 33.0%. The implementation rate of collaboration with home-visiting health teams was 17.4% and with other teams was 10.1%. The implementation rates of periodontal management across stages were as follows: 11.9% for periodontal examination, 18.3% for periodontal treatment, and 11.9% for sustainable periodontal care. Conclusions Periodontal disease programs are predominantly conducted by oral health teams with limited collaboration across other health teams. Additionally, periodontal management activities, such as examinations and treatments, remain insufficient. Integration between oral health teams and other health teams within public health centers or private dental clinics should be improved.

Objective: This study aimed to develop and validate a deep learning (DL) algorithm for the quantitative measurement of thoracolumbar (TL) fracture features, and to evaluate its efficacy across varying levels of clinical expertise. Methods: Using the pretrained Mask Region-Based Convolutional Neural Networks model, originally developed for vertebral body segmentation and fracture detection, we fine-tuned the model and added a new module for measuring fracture metrics—compression rate (CR), Cobb angle (CA), Gardner angle (GA), and sagittal index (SI)—from lumbar spine lateral radiographs. These metrics were derived from six-point labeling by 3 radiologists, forming the ground truth (GT). Training utilized 1,000 nonfractured and 318 fractured radiographs, while validations employed 213 internal and 200 external fractured radiographs. The accuracy of the DL algorithm in quantifying fracture features was evaluated against GT using the intraclass correlation coefficient. Additionally, 4 readers with varying expertise levels, including trainees and an attending spine surgeon, performed measurements with and without DL assistance, and their results were compared to GT and the DL model. Results: The DL algorithm demonstrated good to excellent agreement with GT for CR, CA, GA, and SI in both internal (0.860, 0.944, 0.932, and 0.779, respectively) and external (0.836, 0.940, 0.916, and 0.815, respectively) validations. DL-assisted measurements significantly improved most measurement values, particularly for trainees. Conclusion The DL algorithm was validated as an accurate tool for quantifying TL fracture features using radiographs. DL-assisted measurement is expected to expedite the diagnostic process and enhance reliability, particularly benefiting less experienced clinicians.

Purpose: To date, there is no region-specific guideline for pediatric endoscopy training. This study aimed to illustrate the current status of pediatric endoscopy training in Asia-Pacific region and identify opportunities for improvement. Methods: A cross-sectional survey, using a standardized electronic questionnaire, was conducted among medical schools in the Asia-Pacific region in January 2024. Results: A total of 57 medical centers in 12 countries offering formal Pediatric Gastroenterology training programs participated in this regional survey. More than 75% of the centers had an average case load of <10 cases per week for both diagnostic and therapeutic endoscopies. Only 36% of the study programs employed competency-based outcomes for program development, whereas nearly half (48%) used volume-based curricula.Foreign body retrieval, polypectomy, percutaneous endoscopic gastrostomy, and esophageal variceal hemostasis, that is, sclerotherapy or band ligation (endoscopic variceal sclerotherapy and endoscopic variceal ligation), comprised the top four priorities that the trainees should acquire in the autonomous stage (unconscious) of competence. Regarding the learning environment, only 31.5% provided formal hands-on workshops/simulation training. The direct observation of procedural skills was the most commonly used assessment method. The application of a quality assurance (QA) system in both educational and patient care (Pediatric Endoscopy Quality Improvement Network) aspects was present in only 28% and 17% of the centers, respectively. Conclusion Compared with Western academic societies, the limited availability of cases remains a major concern. To close this gap, simulation and adult endoscopy training are essential. The implementation of reliable and valid assessment tools and QA systems can lead to significant development in future programs.