BACKGROUND: Osteoporosis is the most common metabolic bone disorder. Osteoporosis has emerged as a leading public health problem with elderly persons and its detection is important for prevention and treatment of fracture. this population-based study was conducted to evaluate the prevalence and risk factors of osteoporosis in the elderly women. METHODS: One hundred thirty eight women aged 60 years or older in rural area were investigated with questionnaires and measurements of height, weight. Bone mineral density(BMD) measurements of lumbar spine and femoral neck were made with dual energy X-ray absorptiometry(DEXA). The World Health Organization criteria for diagnosis of osteoporosis using the manufacturer's young adult population mean and our study young population mean have been applied. Our study's young adult population mean was derived using normal premenopausal 37 women aged 30~45 years. RESULTS: The prevalence of osteoporosis was 45.7% for lumbar spine, 13.0% for femoral neck by the manufacture's young adult mean and 63.0% and 34.8% by our study young adult population mean, respectively. Weight and smoking were associated with lumbar spine BMD. Age was associated with femoral neck BMD. CONCLUSION: Our data suggest that the prevalence of osteoporosis is dependant on reference population mean and measurement site.
Aged* ; Diagnosis ; Female ; Femur Neck ; Humans ; Osteoporosis* ; Prevalence* ; Public Health ; Surveys and Questionnaires ; Risk Factors ; Smoke ; Smoking ; Spine ; World Health Organization ; Young Adult

No abstract available.
Periosteum* ; Transforming Growth Factors*

Background: The aim of this study was to assess whether the anteroposterior coverage of the acromion reflecting acromial morphology affects the rotator cuff tear (RCT) and tear size, in addition to the lateral coverage. Methods: Medical records of 356 patients with RCTs, concentric osteoarthritis, and calcific tendinitis identified using threedimensional computed tomography between January 2016 and December 2017 were retrospectively analyzed. The patients were divided into group A (those with RCTs) and group B (those with concentric osteoarthritis or calcific tendinitis). Subsequently, group A was subdivided into three categories according to the size of RCTs: small-to-medium, large, and massive. The lateral coverage was measured through the lateral acromial angle (LAA) and critical shoulder angle (CSA), whereas the anteroposterior coverage was measured via the acromial tilt (AT), acromiohumeral interval (AHI) in the sagittal view, and anteroposterior coverage index (APCI) as a new radiologic parameter. Results: Between groups A and B, CSA (34.5° ± 3.4° and 30.8° ± 3.4°, respectively), APCI (0.83 ± 0.10 and 0.75 ± 0.08, respectively), and AHI (6.3 ± 2.0 mm and 7.8 ± 1.8 mm, respectively) were significantly different (all p < 0.001), whereas LAA and AT did not show a significant difference between the groups (p = 0.089 and p = 0.665, respectively). The independent predictive radiologic parameters of the RCT were the CSA, APCI, and AHI (p < 0.001, p < 0.001, and p = 0.043, respectively); among these, the APCI showed the highest regression coefficient (odds ratio = 2.82). The parameters associated with the size of RCTs were CSA (p = 0.022) and AHI, of which AHI, in particular, had the most significant effect on both small-to-medium and large tears (all p < 0.001). Conclusions Large CSA, high APCI, and low AHI were predictors of RCTs, with the APCI showing the strongest correlation. In addition to the large CSA, low AHI also correlated with the size of RCTs and affected the entire size groups. We suggest that both the lateral coverage and anteroposterior coverage of the acromion should be considered essential factors for predicting the presence of RCTs and tear size.

No abstract available.
Female ; Fibronectins* ; Hypertension, Pregnancy-Induced* ; Plasma* ; Pregnancy*

Anticancer effect and complications were evaluated after transcatheter arterial embolization(TAE) in 12patients with hepatocellular carcinoma until 2 weeks and 4 weeks after TAE, respectively. The results were asfollows: 1. Serum alpha-fetoprotein value decreased in 7 out of 9 patients wih high value prior to TAE. 2. Loss ofenhancement and better definition on enhanced CT were seen in the tumors in all cases, and low-density areas in9/10 . Gas bubbles were seen in low-density areas in 4/10 and highdensity area caused by lipiodol in 6/10. 3.Post-embolization syndrome was develped in most patients but improved clinically within a week after TAE. 4. Onlaboratory examination, impairment of liver function was developed in most patients but improved within 4 weeksafter TAE. 5. Complications on CT included splenic infarction and thickening of wall of the gallbladder, whichdidn't require specific treatment. The authors conclude that TAE for hepatocellular carcinoma reveals apparentanticancer effect on shortterm evaluation, and resultant complications are transient and improved by conservativetreatment.
alpha-Fetoproteins ; Carcinoma, Hepatocellular ; Ethiodized Oil ; Gallbladder ; Humans ; Liver ; Splenic Infarction

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.

Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. Recent research highlights the effectiveness of DL-based convolutional neural networks and machine learning frameworks in diagnosing various shoulder pathologies. Automated image analysis facilitates the accurate assessment of rotator cuff tear size, muscle degeneration, and fatty infiltration in MRI or CT scans, frequently matching or surpassing the accuracy of human experts. Convolutional neural network-based systems are also adept at classifying fractures and joint conditions, enabling the rapid identification of common causes of shoulder pain from plain radiographs. Furthermore, advanced techniques like pose estimation provide precise measurements of the shoulder joint's range of motion and support personalized rehabilitation plans. These automated approaches have also been successful in quantifying local osteoporosis, utilizing machine learning-derived indices to classify bone density status. DL has demonstrated significant potential to improve diagnostic accuracy, efficiency, and consistency in the management of shoulder diseases in older patients. Machine learning-based assessments of imaging data and motion parameters can help clinicians optimize treatment plans and improve patient outcomes. However, to ensure their generalizability, reproducibility, and effective integration into routine clinical workflows, large-scale, prospective validation studies are necessary. As data availability and computational resources increase, the ongoing development of DL-driven applications is expected to further advance and personalize musculoskeletal care, benefiting both healthcare providers and the aging population.