Objective: To assess the effect of transarterial embolization (TAE) for adhesive capsulitis (AC) by evaluating clinical outcomes and changes in inflammation using magnetic resonance imaging (MRI). Materials and Methods: Patients who had undergone TAE between August 2020 and August 2023 for AC refractory to conservative treatments without any invasive procedures for more than 3 months, and had undergone baseline and 3-month post-AC follow-up contrast-enhanced MRI evaluations, were included. A suspension mixture of 500 mg imipenem/cilastatin in 10 mL of iodinated contrast agent was used for TAE. MRI results were analyzed to assess periarticular capsule/ligament inflammation. Clinical assessments included pain scores using the numeric rating scale (NRS) and functional scores using the quick disabilities of the arm, shoulder, and hand (Quick DASH) questionnaire. Results: Twenty-five patients (female:male, 14:11; age, 54.9 ± 7.1 years) were included. Significant reductions in average NRS pain scores as well as improvements in Quick DASH scores and range of motion, including anterior flexion and abduction, were observed at 1, 3, and 6 months after TAE (all P < 0.001). MRI analyses revealed that TAE significantly decreased the grades of axillary recess capsule enhancement, rotator interval (RI) capsule T2 signal intensity, and RI capsule enhancement (all P ≤ 0.004). Conclusion TAE may be an effective and safe therapeutic approach for AC refractory to conservative treatments, alleviating pain and supporting functional recovery. The observed MRI findings suggest that the effectiveness of TAE for AC may be attributed to the reduction of inflammation and the elimination of angiogenesis.

Objective: To assess the effect of transarterial embolization (TAE) for adhesive capsulitis (AC) by evaluating clinical outcomes and changes in inflammation using magnetic resonance imaging (MRI). Materials and Methods: Patients who had undergone TAE between August 2020 and August 2023 for AC refractory to conservative treatments without any invasive procedures for more than 3 months, and had undergone baseline and 3-month post-AC follow-up contrast-enhanced MRI evaluations, were included. A suspension mixture of 500 mg imipenem/cilastatin in 10 mL of iodinated contrast agent was used for TAE. MRI results were analyzed to assess periarticular capsule/ligament inflammation. Clinical assessments included pain scores using the numeric rating scale (NRS) and functional scores using the quick disabilities of the arm, shoulder, and hand (Quick DASH) questionnaire. Results: Twenty-five patients (female:male, 14:11; age, 54.9 ± 7.1 years) were included. Significant reductions in average NRS pain scores as well as improvements in Quick DASH scores and range of motion, including anterior flexion and abduction, were observed at 1, 3, and 6 months after TAE (all P < 0.001). MRI analyses revealed that TAE significantly decreased the grades of axillary recess capsule enhancement, rotator interval (RI) capsule T2 signal intensity, and RI capsule enhancement (all P ≤ 0.004). Conclusion TAE may be an effective and safe therapeutic approach for AC refractory to conservative treatments, alleviating pain and supporting functional recovery. The observed MRI findings suggest that the effectiveness of TAE for AC may be attributed to the reduction of inflammation and the elimination of angiogenesis.

Objective: To assess the effect of transarterial embolization (TAE) for adhesive capsulitis (AC) by evaluating clinical outcomes and changes in inflammation using magnetic resonance imaging (MRI). Materials and Methods: Patients who had undergone TAE between August 2020 and August 2023 for AC refractory to conservative treatments without any invasive procedures for more than 3 months, and had undergone baseline and 3-month post-AC follow-up contrast-enhanced MRI evaluations, were included. A suspension mixture of 500 mg imipenem/cilastatin in 10 mL of iodinated contrast agent was used for TAE. MRI results were analyzed to assess periarticular capsule/ligament inflammation. Clinical assessments included pain scores using the numeric rating scale (NRS) and functional scores using the quick disabilities of the arm, shoulder, and hand (Quick DASH) questionnaire. Results: Twenty-five patients (female:male, 14:11; age, 54.9 ± 7.1 years) were included. Significant reductions in average NRS pain scores as well as improvements in Quick DASH scores and range of motion, including anterior flexion and abduction, were observed at 1, 3, and 6 months after TAE (all P < 0.001). MRI analyses revealed that TAE significantly decreased the grades of axillary recess capsule enhancement, rotator interval (RI) capsule T2 signal intensity, and RI capsule enhancement (all P ≤ 0.004). Conclusion TAE may be an effective and safe therapeutic approach for AC refractory to conservative treatments, alleviating pain and supporting functional recovery. The observed MRI findings suggest that the effectiveness of TAE for AC may be attributed to the reduction of inflammation and the elimination of angiogenesis.

Objective: To assess the effect of transarterial embolization (TAE) for adhesive capsulitis (AC) by evaluating clinical outcomes and changes in inflammation using magnetic resonance imaging (MRI). Materials and Methods: Patients who had undergone TAE between August 2020 and August 2023 for AC refractory to conservative treatments without any invasive procedures for more than 3 months, and had undergone baseline and 3-month post-AC follow-up contrast-enhanced MRI evaluations, were included. A suspension mixture of 500 mg imipenem/cilastatin in 10 mL of iodinated contrast agent was used for TAE. MRI results were analyzed to assess periarticular capsule/ligament inflammation. Clinical assessments included pain scores using the numeric rating scale (NRS) and functional scores using the quick disabilities of the arm, shoulder, and hand (Quick DASH) questionnaire. Results: Twenty-five patients (female:male, 14:11; age, 54.9 ± 7.1 years) were included. Significant reductions in average NRS pain scores as well as improvements in Quick DASH scores and range of motion, including anterior flexion and abduction, were observed at 1, 3, and 6 months after TAE (all P < 0.001). MRI analyses revealed that TAE significantly decreased the grades of axillary recess capsule enhancement, rotator interval (RI) capsule T2 signal intensity, and RI capsule enhancement (all P ≤ 0.004). Conclusion TAE may be an effective and safe therapeutic approach for AC refractory to conservative treatments, alleviating pain and supporting functional recovery. The observed MRI findings suggest that the effectiveness of TAE for AC may be attributed to the reduction of inflammation and the elimination of angiogenesis.

Objective: To assess the effect of transarterial embolization (TAE) for adhesive capsulitis (AC) by evaluating clinical outcomes and changes in inflammation using magnetic resonance imaging (MRI). Materials and Methods: Patients who had undergone TAE between August 2020 and August 2023 for AC refractory to conservative treatments without any invasive procedures for more than 3 months, and had undergone baseline and 3-month post-AC follow-up contrast-enhanced MRI evaluations, were included. A suspension mixture of 500 mg imipenem/cilastatin in 10 mL of iodinated contrast agent was used for TAE. MRI results were analyzed to assess periarticular capsule/ligament inflammation. Clinical assessments included pain scores using the numeric rating scale (NRS) and functional scores using the quick disabilities of the arm, shoulder, and hand (Quick DASH) questionnaire. Results: Twenty-five patients (female:male, 14:11; age, 54.9 ± 7.1 years) were included. Significant reductions in average NRS pain scores as well as improvements in Quick DASH scores and range of motion, including anterior flexion and abduction, were observed at 1, 3, and 6 months after TAE (all P < 0.001). MRI analyses revealed that TAE significantly decreased the grades of axillary recess capsule enhancement, rotator interval (RI) capsule T2 signal intensity, and RI capsule enhancement (all P ≤ 0.004). Conclusion TAE may be an effective and safe therapeutic approach for AC refractory to conservative treatments, alleviating pain and supporting functional recovery. The observed MRI findings suggest that the effectiveness of TAE for AC may be attributed to the reduction of inflammation and the elimination of angiogenesis.

Ulcerative colitis (UC) is a chronic inflammation of the gastrointestinal tract and is characterized by alternating periods of inflammation and remission. Although UC incidence is lower in Taiwan than in Western countries, its impact remains considerable, demanding updated guidelines for addressing local healthcare challenges and patient needs. The revised guidelines employ international standards and recent research, emphasizing practical implementation within the Taiwanese healthcare system. Since the inception of the guidelines in 2017, the Taiwan Society of Inflammatory Bowel Disease has acknowledged the need for ongoing revisions to incorporate emerging therapeutic options and evolving disease management practices. This updated guideline aims to align UC management with local contexts, ensuring comprehensive and context-specific recommendations, thereby raising the standard of care for UC patients in Taiwan. By adapting and optimizing international protocols for local relevance, these efforts seek to enhance health outcomes for patients with UC.

Crohn’s disease (CD) is a chronic, fluctuating inflammatory condition that primarily affects the gastrointestinal tract. Although the incidence of CD in Taiwan is lower than that in Western countries, the severity of CD presentation appears to be similar between Asia and the West. This observation indicates the urgency for devising revised guidelines tailored to the unique reimbursement system, and patient requirements in Taiwan. The core objectives of these updated guidelines include the updated treatment choices and the integration of the treat-to-target strategy into CD management, promoting the achievement of deep remission to mitigate complications and enhance the overall quality of life. Given the diversity in disease prevalence, severity, insurance policies, and access to medical treatments in Taiwan, a customized approach is imperative for formulating these guidelines. Such tailored strategies ensure that international standards are not only adapted but also optimized to local contexts. Since the inception of its initial guidelines in 2017, the Taiwan Society of Inflammatory Bowel Disease (TSIBD) has acknowledged the importance of continuous revisions for incorporating new therapeutic options and evolving disease management practices. The latest update leverages international standards and recent research findings focused on practical implementation within the Taiwanese healthcare system.

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.

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.

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.