Objectives: This study aimed to develop an accurate pediatric bone age prediction model by utilizing deep learning models and contrast conversion techniques, in order to improve growth assessment and clinical decision-making in clinical practice. Methods: The study employed a variety of deep learning models and contrast conversion techniques to predict bone age. The training dataset consisted of pediatric left-hand X-ray images, each annotated with bone age and sex information. Deep learning models, including a convolutional neural network , Residual Network 50 , Visual Geometry Group 19, Inception V3, and Xception were trained and assessed using the mean absolute error (MAE). For the test data, contrast conversion techniques including fuzzy contrast enhancement, contrast limited adaptive histogram equalization (HE) , and HE were implemented. The quality of the images was evaluated using peak signal-to-noise ratio (SNR), mean squared error, SNR, coefficient of variation, and contrast-to-noise ratio metrics. The bone age prediction results using the test data were evaluated based on the MAE and root mean square error, and the t-test was performed. Results: The Xception model showed the best performance (MAE=41.12). HE exhibited superior image quality, with higher SNR and coefficient of variation values than other methods. Additionally, HE demonstrated the highest contrast among the techniques assessed, with a contrast-to-noise ratio value of 1.29. Improvements in bone age prediction resulted in a decline in MAE from 2.11 to 0.24, along with a decrease in root mean square error from 0.21 to 0.02. Conclusion This study demonstrates that preprocessing the data before model training does not significantly affect the performance of bone age prediction when comparing contrast-converted images with original images.

The antinuclear antbody (ANA) test have been used to screen the patients with systemic lupus erythematosus (SLE) and other autoimmune diseases. We had retrospectively reviewed the 263 records of pediatric patients with doing ANA tests who admitted at Department of Pediatrics, Kyung Hee University Hospital, from January 1988 to May 1993. The following results were obtained. 1) The positive rate of ANA test in patients with connective tissue diseases is 16 out of 40(40%).In patients with SLE, the positive rate of ANA test is 9 out of 11 (82%). 2) The positive predictivity for SLE is 9 out 36 (25%). 3) The positive predictivity for connective tissue disease and possible immune disease is 28 out of 36 (78%). 4) The false positive rate is 8 0ut of 36 (22%), Thus, the pediatric patients with positive ANA test should be applicable for diagnosis with prudence. 5) The positive anti-dsDNA in patients with the positive ANA is shown in 4 cases and these patients are all SLE. In conclusion, the patients who had repeated positive ANA should be tested Anti-dsDNA antibody, and further clinical and diagnostic evaluation of other ANA associated diseases.
Autoimmune Diseases ; Connective Tissue Diseases ; Diagnosis ; Humans ; Immune System Diseases ; Lupus Erythematosus, Systemic ; Pediatrics ; Retrospective Studies

BACKGROUND: The inclusion of cardiopulmonary resuscitation (CPR) in formal education has been a useful approach to providing basic life support (BLS) services. However, because not all students have been able to learn directly from certified instructors, we studied the educational efficacy of the use of peer-assisted learning (PAL) to train high-school students to perform BLS services. METHODS: This study consisted of 187 high-school students: 68 participants served as a control group and received a 1-hour BLS training from a school nurse, and 119 were included in a PAL group and received a 1-hour CPR training from a PAL leader. Participants' BLS training was preceded by the completion of questionnaires regarding their background. Three months after the training, the participants were asked to respond to questionnaires about their willingness to perform CPR on bystander CPR and their retention of knowledge of BLS. RESULTS: We found no statistically significant difference between the control and PAL groups in their willingness to perform CPR on bystanders (control: 55.2％, PAL: 64.7％,P=0.202). The PAL group was not significantly different from the control group (control: 60.78±39.77, PAL: 61.76±17.80, P=0.848) in retention of knowledge about BLS services. CONCLUSION: In educating high school students about BLS, there was no significant difference between PAL and traditional education in increasing the willingness to provide CPR to bystanders or the ability to retain knowledge about BLS.

Purpose: The least constrained prosthesis is generally recommended in primary total knee arthroplasty (TKA). Nevertheless, a varus/valgus constrained (VVC) prosthesis should be implanted when a semi-constrained prosthesis is not good for adequate stability, especially in the coronal plane. In domestic situations, however, the VVC prosthesis could not always be prepared for every primary TKA case. Therefore, it is sometimes impractical to use a VVC prosthesis for unsual unstable situations. This study provides information for preparing VVC prostheses in the preoperative planning of primary TKA through an analysis of primary VVC TKA cases. Materials and Methods: This study reviewed 1,797 primary TKAs, performed between May 2003 and February 2016. The reasons for requiring VVC prosthesis and the preoperative conditions in 29 TKAs that underwent primary TKA with a VVC prosthesis were analyzed retrospectively. Results: In primary TKA, 29 cases (1.6%) in 27 patients (6 male and 21 female) used VVC prosthesis. Two patients underwent a VVC prosthesis on both knees. The mean age of the patients was 63.4 years old (34–79 years). The mean flexion contracture was 16.2° (-20°–90°), and the mean angle of great flexion was 111.7° (35°–145°). The situations requiring a VVC prosthesis were severe valgus deformity in 10 knees, knee stiffness requiring extensive soft tissue release in 10 knees, previously injured collateral ligaments in five knees, and distal femoral bone defect due to avascular necrosis in four knees. The mean tibiofemoral angle was 25.7° (21°–43°) in 10 cases with a valgus deformity. The mean flexion contracture was 37.5° (20°–90°), and the mean range of motion was 48.5° (10°–70°) in 10 cases with knee stiffness. Conclusion The preparation of VVC prosthesis is recommended, even for primary TKA in cases of severe valgus deformity (tibiofemoral angle>20°), stiff knee (the range of motion: less than 70° with more than 20° flexion contracture), and the cases with a previous collateral ligament injury. This information will help in the preparation of adequate TKA prostheses for unusual unstable situations.

Purpose: This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies. Methods: The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration. Results: The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible. Conclusions This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Purpose: This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies. Methods: The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration. Results: The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible. Conclusions This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.

Purpose: This study aims to develop a comprehensive preprocessing workflow for Digital Imaging and Communications in Medicine (DICOM) files to facilitate their effective use in AI-driven medical applications. With the increasing utilization of DICOM data for AI learning, analysis, Metaverse platform integration, and 3D printing of anatomical structures, the need for streamlined preprocessing is essential. The workflow is designed to optimize DICOM files for diverse applications, improving their usability and accessibility for advanced medical technologies. Methods: The proposed workflow employs a systematic approach to preprocess DICOM files for AI applications, focusing on noise reduction, normalization, segmentation, and conversion to 3D-renderable formats. These steps are integrated into a unified process to address challenges such as data variability, format incompatibilities, and high computational demands. The studyincorporates real-world medical imaging datasets to evaluate the workflow’s effectiveness and adaptability for AI analysis and 3D visualization. Additionally, the workflow’s compatibility withvirtual environments, such as Metaverse platforms, is assessed to ensure seamless integration. Results: The implementation of the workflow demonstrated significant improvements in the preprocessing of DICOM files. The processed files were optimized for AI analysis, yielding enhanced model performance and accuracy in learning tasks. Furthermore, the workflow enabled the successful conversion of DICOM data into 3D-printable formats and virtual environments, supporting applications like anatomical visualization and simulation. The study highlights the workflow's ability to reduce preprocessing time and errors, making advanced medical imaging technologies more accessible. Conclusions This study emphasizes the critical role of effective preprocessing in maximizing the potential of DICOM data for AI-driven applications and innovative medical solutions. The proposed workflow simplifies the preprocessing of DICOM files, facilitating their integration into AI models, Metaverse platforms, and 3D printing processes. By enhancing usability and accessibility, the workflow fosters broader adoption of advanced imaging technologies in the medical field.

Purpose: The standardized uptake value (SUV) is a key quantitative index in nuclear medicine imaging; however, variations in region‐of‐interest (ROI) determination exist across institutions. This study aims to standardize SUV evaluation by introducing a deep learning‐based quantitative analysis method that enhances diagnostic and prognostic accuracy. Methods: We used the Swin UNETR model to automatically segment key organs (breast, liver, spleen, and bone marrow) critical for breast cancer prognosis. Tumor segmentation was performed iteratively based on predefined SUV thresholds, and prognostic information was extracted from the liver, spleen, and bone marrow (reticuloendothelial system). The artificial intelligence training process employed 3 datasets: a test dataset (40 patients), a validation dataset (10 patients), and an independent test dataset (10 patients). To validate our approach, we compared the SUV values obtained using our method with those produced by commercial software. Results: In a dataset of 10 patients, our method achieved an auto‐segmentation accuracy of 0.9311 for all target organs. Comparison of maximum SUV and mean SUV values from our automated segmentation with those from traditional single‐ROI methods revealed differences of 0.19 and 0.16, respectively, demonstrating improved reliability and accuracy in whole‐organ SUV analysis. Conclusion This study successfully standardized SUV calculation in nuclear medicine imaging through deep learning‐based automated organ segmentation and SUV analysis, significantly enhancing accuracy in predicting breast cancer prognosis.