Objective: To compare the quality of deep learning-reconstructed turbo spin-echo (DL-TSE) and conventionally interpolated turbo spin-echo (Conv-TSE) techniques in contrast-enhanced MRI of the neck. Materials and Methods: Contrast-enhanced T1-weighted DL-TSE and Conv-TSE images were acquired using 3T scanners from 106 patients. DL-TSE employed a closed-source, ‘work-in-progress’ (WIP No. 1062, iTSE, version 10; Siemens Healthineers) algorithm for interpolation and denoising to achieve the same in-plane resolution (axial: 0.26 x 0.26 mm 2 ; coronal: 0.29 x 0.29 mm 2 ) while reducing scan times by 15.9% and 52.6% for axial and coronal scans, respectively. The full width at half maximum (FWHM) and percent signal ghosting were measured using stationary and flow phantom scans, respectively. In patient images, non-uniformity (NU), contrast-to-noise ratio (CNR), and regional mucosal FWHM were evaluated. Two neuroradiologists visually rated the patient images for overall quality, sharpness, regional mucosal conspicuity, artifacts, and lesions using a 5-point Likert scale. Results: FWHM in the stationary phantom scan was consistently sharper in DL-TSE. The percent signal ghosting outside the flow phantom was lower in DL-TSE (0.06% vs. 0.14%) but higher within the phantom (8.92% vs. 1.75%) compared to ConvTSE. In patient scans, DL-TSE showed non-inferior NU and higher CNR. Regional mucosal FWHM was significantly better in DL-TSE, particularly in the oropharynx (coronal: 1.08 ± 0.31 vs. 1.52 ± 0.46 mm) and hypopharynx (coronal: 1.26 ± 0.35 vs. 1.91 ± 0.56 mm) (both P < 0.001). DL-TSE demonstrated higher overall image quality (axial: 4.61 ± 0.49 vs. 3.32 ± 0.54) and sharpness (axial: 4.40 ± 0.56 vs. 3.11 ± 0.53) (both P < 0.001). In addition, mucosal conspicuity was improved, especially in the oropharynx (axial: 4.41 ± 0.67 vs. 3.40 ± 0.69) and hypopharynx (axial: 4.45 ± 0.58 vs. 3.58 ± 0.63) (both P < 0.001).Extracorporeal ghost artifacts were reduced in DL-TSE (axial: 4.32 ± 0.60 vs. 3.90 ± 0.71, P < 0.001) but artifacts overlapping anatomical structures were slightly more pronounced (axial: 3.78 ± 0.74 vs. 3.95 ± 0.72, P < 0.001). Lesions were detected with higher confidence in DL-TSE. Conclusion DL-based reconstruction applied to accelerated neck MRI improves overall image quality, sharpness, mucosal conspicuity in motion-prone regions, and lesion detection confidence. Despite more pronounced ghost artifacts overlapping anatomical structures, DL-TSE enables substantial scan time reduction while enhancing diagnostic performance.

Objective: This study aimed to compare the efficacy and safety of romosozumab, a bone anabolic agent, versus vertebroplasty, a conventional surgical intervention, in treating osteoporotic vertebral compression fractures (OVCFs). Methods: A retrospective analysis included 86 thoracic/lumbar compression fracture patients from 2014 to 2022 at a medical center. Forty-two patients received romosozumab (monthly injections for 1 year) followed by 1 year of denosumab, while 44 underwent vertebroplasty followed by denosumab injections biannually for 2 years. Outcomes were assessed using the Numerical Rating Scale (NRS) for pain, bone mineral density (BMD), vertebral compression ratio, and Cobb angle over 12 months. Results: At 12 months, the romosozumab group showed a greater reduction in NRS scores (4.90 ± 1.01 vs. 4.27 ± 1.34, p = 0.015) and a higher increase in lumbar BMD (0.8 ± 0.5 vs. 0.5 ± 0.3, p = 0.000) compared to the vertebroplasty group. There were no significant differences in changes in hip total BMD and femur neck BMD (p = 0.190, p = 0.167, respectively). Radiographic assessments showed no significant differences in vertebral compression ratio (14.7% vs. 14.8%; p = 0.960) or Cobb angle (4.2° vs. 4.9°; p = 0.302). The incidence of major osteoporotic fractures was lower in the romosozumab group (7.1% vs. 25.0%, p = 0.051), with similar rates of cardiovascular events in both groups (4.8% vs. 9.1%, p = 0.716). Conclusion Romosozumab has demonstrated superior pain reduction and lumbar BMD improvement compared to vertebroplasty at 12 months, with no significant differences in radiographic outcomes or adverse events, suggesting it as an alternative to vertebroplasty for OVCF.

Purpose: The purpose of this study is to conduct job analysis of nurses at dementia care centers and to identify the importance, frequency, and difficulty of each duty and task. Methods: Through Developing a Curriculum (DACUM) Committee workshop, the committee members developed a job analysis tool using DACUM, and the nurses working at dementia care centers evaluated the importance, frequency, and difficulty of each duty and task. Results: The jobs of the nurses were derived from 10 duties and 66 tasks, and each duty consisted of 3 to 10 tasks. The important duties were ‘public guardianship project for dementia’ and ‘dementia diagnosis screening,’ the most frequent duties were ‘consultation and registration management,’ and ‘dementia diagnosis screening,’ and the most difficult duties were ‘public guardianship project for dementia’ and ‘project planning and evaluation.’ Based on these results, the core duties and tasks were derived, and the top priority duties were ‘consultation and registration management,’ ‘case management,’ and ‘support for families and carers of dementia patients’. Conclusion The most recent duties of nurses, who have the largest proportion of workers at dementia care centers, were identified, and the core duties that should be given priority in selecting the direction of education for job performance and professional improvement were presented. Based on the application method of education and training presented in this study, it is important to detail education and training that is appropriate for and applicable to each duty to support the professionalism of nurses at dementia care centers.

Objective: To compare the quality of deep learning-reconstructed turbo spin-echo (DL-TSE) and conventionally interpolated turbo spin-echo (Conv-TSE) techniques in contrast-enhanced MRI of the neck. Materials and Methods: Contrast-enhanced T1-weighted DL-TSE and Conv-TSE images were acquired using 3T scanners from 106 patients. DL-TSE employed a closed-source, ‘work-in-progress’ (WIP No. 1062, iTSE, version 10; Siemens Healthineers) algorithm for interpolation and denoising to achieve the same in-plane resolution (axial: 0.26 x 0.26 mm 2 ; coronal: 0.29 x 0.29 mm 2 ) while reducing scan times by 15.9% and 52.6% for axial and coronal scans, respectively. The full width at half maximum (FWHM) and percent signal ghosting were measured using stationary and flow phantom scans, respectively. In patient images, non-uniformity (NU), contrast-to-noise ratio (CNR), and regional mucosal FWHM were evaluated. Two neuroradiologists visually rated the patient images for overall quality, sharpness, regional mucosal conspicuity, artifacts, and lesions using a 5-point Likert scale. Results: FWHM in the stationary phantom scan was consistently sharper in DL-TSE. The percent signal ghosting outside the flow phantom was lower in DL-TSE (0.06% vs. 0.14%) but higher within the phantom (8.92% vs. 1.75%) compared to ConvTSE. In patient scans, DL-TSE showed non-inferior NU and higher CNR. Regional mucosal FWHM was significantly better in DL-TSE, particularly in the oropharynx (coronal: 1.08 ± 0.31 vs. 1.52 ± 0.46 mm) and hypopharynx (coronal: 1.26 ± 0.35 vs. 1.91 ± 0.56 mm) (both P < 0.001). DL-TSE demonstrated higher overall image quality (axial: 4.61 ± 0.49 vs. 3.32 ± 0.54) and sharpness (axial: 4.40 ± 0.56 vs. 3.11 ± 0.53) (both P < 0.001). In addition, mucosal conspicuity was improved, especially in the oropharynx (axial: 4.41 ± 0.67 vs. 3.40 ± 0.69) and hypopharynx (axial: 4.45 ± 0.58 vs. 3.58 ± 0.63) (both P < 0.001).Extracorporeal ghost artifacts were reduced in DL-TSE (axial: 4.32 ± 0.60 vs. 3.90 ± 0.71, P < 0.001) but artifacts overlapping anatomical structures were slightly more pronounced (axial: 3.78 ± 0.74 vs. 3.95 ± 0.72, P < 0.001). Lesions were detected with higher confidence in DL-TSE. Conclusion DL-based reconstruction applied to accelerated neck MRI improves overall image quality, sharpness, mucosal conspicuity in motion-prone regions, and lesion detection confidence. Despite more pronounced ghost artifacts overlapping anatomical structures, DL-TSE enables substantial scan time reduction while enhancing diagnostic performance.

Objective: This study aimed to compare the efficacy and safety of romosozumab, a bone anabolic agent, versus vertebroplasty, a conventional surgical intervention, in treating osteoporotic vertebral compression fractures (OVCFs). Methods: A retrospective analysis included 86 thoracic/lumbar compression fracture patients from 2014 to 2022 at a medical center. Forty-two patients received romosozumab (monthly injections for 1 year) followed by 1 year of denosumab, while 44 underwent vertebroplasty followed by denosumab injections biannually for 2 years. Outcomes were assessed using the Numerical Rating Scale (NRS) for pain, bone mineral density (BMD), vertebral compression ratio, and Cobb angle over 12 months. Results: At 12 months, the romosozumab group showed a greater reduction in NRS scores (4.90 ± 1.01 vs. 4.27 ± 1.34, p = 0.015) and a higher increase in lumbar BMD (0.8 ± 0.5 vs. 0.5 ± 0.3, p = 0.000) compared to the vertebroplasty group. There were no significant differences in changes in hip total BMD and femur neck BMD (p = 0.190, p = 0.167, respectively). Radiographic assessments showed no significant differences in vertebral compression ratio (14.7% vs. 14.8%; p = 0.960) or Cobb angle (4.2° vs. 4.9°; p = 0.302). The incidence of major osteoporotic fractures was lower in the romosozumab group (7.1% vs. 25.0%, p = 0.051), with similar rates of cardiovascular events in both groups (4.8% vs. 9.1%, p = 0.716). Conclusion Romosozumab has demonstrated superior pain reduction and lumbar BMD improvement compared to vertebroplasty at 12 months, with no significant differences in radiographic outcomes or adverse events, suggesting it as an alternative to vertebroplasty for OVCF.

Purpose: The purpose of this study is to conduct job analysis of nurses at dementia care centers and to identify the importance, frequency, and difficulty of each duty and task. Methods: Through Developing a Curriculum (DACUM) Committee workshop, the committee members developed a job analysis tool using DACUM, and the nurses working at dementia care centers evaluated the importance, frequency, and difficulty of each duty and task. Results: The jobs of the nurses were derived from 10 duties and 66 tasks, and each duty consisted of 3 to 10 tasks. The important duties were ‘public guardianship project for dementia’ and ‘dementia diagnosis screening,’ the most frequent duties were ‘consultation and registration management,’ and ‘dementia diagnosis screening,’ and the most difficult duties were ‘public guardianship project for dementia’ and ‘project planning and evaluation.’ Based on these results, the core duties and tasks were derived, and the top priority duties were ‘consultation and registration management,’ ‘case management,’ and ‘support for families and carers of dementia patients’. Conclusion The most recent duties of nurses, who have the largest proportion of workers at dementia care centers, were identified, and the core duties that should be given priority in selecting the direction of education for job performance and professional improvement were presented. Based on the application method of education and training presented in this study, it is important to detail education and training that is appropriate for and applicable to each duty to support the professionalism of nurses at dementia care centers.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.

This study aimed to assess the utilization trends of pulmonary rehabilitation (PR) among lung cancer patients in Korea using the National Health Insurance Service (NHIS) database (2017 to 2021). PR was introduced and covered under the NHIS in 2016, primarily for chronic obstructive pulmonary disease, but recent evidence suggests its benefits for lung cancer patients. Data extraction was based on Korea Informative Classification of Diseases 8th revision codes C33 and C34, with PR prescriptions identified by codes MM440 and MM290.Descriptive statistical analysis was performed, and propensity score matching was used for comparison between PR and non-PR groups. Results showed a significant increase in PR utilization, with the number of patients receiving PR (MM440) rising from 1,002 in 2017 to 3,723 in 2021, indicating a 3.7-fold increase. However, the proportion of patients receiving PR remained low at 2.9% in 2021. Enhanced access to PR services and improved evaluation strategies are essential for optimizing patient outcomes.

Objective: : In degenerative lumbar spondylolisthesis, interbody fusion surgery (IFS) has long been recommended as the gold standard of surgical management. However, IFS is less recommended for high-risk patients such as the elderly because it involves extensive surgery, with a long operation time and high volumes of blood loss, which lead to marked perioperative morbidity. We report an alternative primary and salvage treatment technique for high-risk lumbar spondylolisthesis through posterior lumbar element reinforcement using interspinous fixation and decompression alone without interbody fusion. Methods: : Plain radiographs, computed tomography scans, and magnetic resonance imaging, taken at different intervals, were used to measure local disc height (DH), vertebral body slippage (BS), and segmental motion angle (SMA). A Visual analogue scale and the Oswestry disability index (ODI) were applied pre-operation and at the last follow-up. Results: : The local SMA decreased significantly by 3.46°±3.07°, from 10.61°±3.42° preoperatively to 7.15±3.70 at the last follow-up (p<0.001). The DH decreased from 8.61±2.88 mm preoperatively to 8.41±2.48 mm at the last follow-up (p=0.074). The BS decreased from 3.49±4.29 mm preoperatively to 3.41±4.91 mm at the last follow-up (p=0.092). None of the patients reported worsening pain or an increased ODI after surgery, and there were no surgery-related complications. Conclusion : Posterior lumbar element reinforcement by decompression alone with SPIRE™ fixation is an alternative primary and salvage treatment option for select patients with spondylolisthesis.

Purpose: To estimate the sample size required for developing artificial intelligence (AI) that can predict soft-tissue and alveolar bone changes following orthodontic treatment. Materials and Methods: From the original data sets with N=887, consisting of 132 input and 88 output variables used to create AI models for predicting treatment changes following orthodontic treatment, six subsets of the data (n=75, 150, 300, 450, 600, and 750) were generated through random resampling procedures. The process was repeated four times, resulting in 24 different data subsets. Each data subset was used to create a total of 24 AI models using the TabNet deep neural network algorithm. The clinically acceptable prediction accuracy was defined as a less than 1.5 mm prediction error on the lower lip. The prediction errors from each AI model were compared according to sample sizes and analyzed to estimate the optimal sample size. Results: The prediction error decreased with increasing sample sizes. A training sample size greater than approximately 1650 was estimated to develop an AI model with less than 1.5 mm of prediction errors at the lower lip area. Conclusion From a statistical and research design perspective, a considerable amount of training data appears necessary to develop an AI prediction model with clinically acceptable accuracy.