Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

Objective: Healthcare workers (HCWs) suffered from a heavy mental health burden during the coronavirus disease-2019 (COVID-19) pandemic. We aimed to explore the differences in sleep disturbance in three waves of the COVID-19 pandemic in Taiwan among HCWs. Moreover, factors associated with sleep disturbances in the third wave were investigated. Methods: This study, with three waves of cross-sectional surveys, recruited first-line and second-line HCWs. The level of sleep disturbance and related demographic variables were collected through self-report questionnaires. Differences in sleep disturbance across the three waves were compared with analysis of variance. Factors associated with the level of sleep disturbance were identified using univariate linear regression and further used for multivariate stepwise and bootstrap linear regression to identify the independent predictors. Results: In total, 711, 560, and 747 HCWs were included in the first, second, and third waves, respectively. For first-line HCWs, sleep disturbance was significantly higher in the third wave than in the first wave. The level of sleep disturbance gradually increased across the three waves for all HCWs. In addition, sleep disturbance was associated with depression, posttraumatic stress disorder (PTSD) symptoms, anxiety about COVID-19, vaccine mistrust, and poorer physical and mental health among first-line HCWs. Among second-line HCWs, sleep disturbance was associated with younger age, depression, PTSD symptoms, lower preference for natural immunity, and poorer physical health. Conclusion The current study identified an increase in sleep disturbance and several predictors among HCWs. Further investigation is warranted to extend the application and generalizability of the current study.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objectives: Concerns about erythropoietin (EPO) therapy for anemia in patients with end-stage renal disease (ESRD) contributing to potential bone loss and increased fracture risks are growing. This study investigated the impact of EPO administration on the risk of common osteoporotic fractures in ESRD patients. Methods: This population-based retrospective cohort study compared EPO users and non-EPO users among ESRD patients undergoing hemodialysis, diagnosed with ESRD between 2000 and 2014 identified from the National Health Insurance Research Database of Taiwan. The cohorts were matched at a propensity score ratio of 1:1, resulting in equal sample sizes of 2839. Variables related to comorbidities were considered. Results: EPO users exhibited higher cumulative incidences of major osteoporotic fractures, hip fractures, spine fractures, and wrist fractures compared with the non-EPO user (all P < 0.001). In adjusted Cox regression models, higher adjusted subdistribution hazard ratios (aSHRs) were observed for major osteoporotic fractures (2.41, 95% confidence interval [CI] = 2.01–2.89), osteoporotic hip fractures (2.19, 95% CI = 1.69–2.85), spine fractures (2.50, 95% CI = 1.87–3.34), and wrist fractures (2.34, 95% CI = 1.44–3.78) in EPO users than in nonEPO users. The risk of major osteoporotic fractures significantly increased with increasing EPO doses (P for trend < 0.0001), and a similar trend was observed for the risks of osteoporotic spine and wrist fractures. Conclusions Our findings suggest that EPO treatment in patients with ESRD undergoing hemodialysis is associated with an increased risk of osteoporotic fractures.