1.Transforaminal interbody debridement and fusion with antibiotic-impregnated bone graft to treat pyogenic discitis and vertebral osteomyelitis: a comparative study in Asian population
Chao-Chien CHANG ; Hsiao-Kang CHANG ; Meng-Ling LU ; Adam WEGNER ; Re-Wen WU ; Tsung-Cheng YIN
Asian Spine Journal 2025;19(1):38-45
Methods:
Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups.
Results:
Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°.
Conclusions
TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.
2.Transforaminal interbody debridement and fusion with antibiotic-impregnated bone graft to treat pyogenic discitis and vertebral osteomyelitis: a comparative study in Asian population
Chao-Chien CHANG ; Hsiao-Kang CHANG ; Meng-Ling LU ; Adam WEGNER ; Re-Wen WU ; Tsung-Cheng YIN
Asian Spine Journal 2025;19(1):38-45
Methods:
Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups.
Results:
Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°.
Conclusions
TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.
3.Transforaminal interbody debridement and fusion with antibiotic-impregnated bone graft to treat pyogenic discitis and vertebral osteomyelitis: a comparative study in Asian population
Chao-Chien CHANG ; Hsiao-Kang CHANG ; Meng-Ling LU ; Adam WEGNER ; Re-Wen WU ; Tsung-Cheng YIN
Asian Spine Journal 2025;19(1):38-45
Methods:
Thirty patients with PDVO of the lumbar or thoracic spine treated with transforaminal interbody debridement and fusion (TIDF) with AIBG between March 2014 and May 2022 were reviewed (AIBG group). For comparative analysis, 28 PDVO patients who underwent TIDF without AIBG between January 2009 and June 2011 were enrolled (non-AIBG group). The minimum follow-up duration was 2 years. Clinical characteristics and surgical indications were comparable in the two groups. C-reactive protein (CRP) levels and the postoperative antibiotics course were compared between the two groups.
Results:
Surgical treatment for PDVO resulted in clinical improvement and adequate infection control. Despite the shorter postoperative intravenous antibiotic duration (mean: 19.0 days vs. 39.8 days), the AIBG group had significantly lower CRP levels at postoperative 4 and 6 weeks. The mean Visual Analog Scale pain scores improved from 7.3 preoperatively to 2.2 at 6 weeks postoperatively. The average angle correction at the last follow-up was 7.9°.
Conclusions
TIDF with AIBG for PDVO can achieve local infection control with a faster reduction in CRP levels, leading to a shorter antibiotic duration.
4.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.
5.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.
6.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.
7.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.
8.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.
9.Taiwan Association for the Study of the Liver-Taiwan Society of Cardiology Taiwan position statement for the management of metabolic dysfunction- associated fatty liver disease and cardiovascular diseases
Pin-Nan CHENG ; Wen-Jone CHEN ; Charles Jia-Yin HOU ; Chih-Lin LIN ; Ming-Ling CHANG ; Chia-Chi WANG ; Wei-Ting CHANG ; Chao-Yung WANG ; Chun-Yen LIN ; Chung-Lieh HUNG ; Cheng-Yuan PENG ; Ming-Lung YU ; Ting-Hsing CHAO ; Jee-Fu HUANG ; Yi-Hsiang HUANG ; Chi-Yi CHEN ; Chern-En CHIANG ; Han-Chieh LIN ; Yi-Heng LI ; Tsung-Hsien LIN ; Jia-Horng KAO ; Tzung-Dau WANG ; Ping-Yen LIU ; Yen-Wen WU ; Chun-Jen LIU
Clinical and Molecular Hepatology 2024;30(1):16-36
Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.
10.Extracorporeal shock wave therapy in cubital tunnel syndrome: A pilot study
Yu-Ping Shen ; Yin-Yin Wu ; Heng-Yi Chu ; Tsung-Ying Li ; Liang-Cheng Chen ; Yung-Tsan Wu
Neurology Asia 2018;23(3):233-238
To investigate the clinical therapeutic effect of extracorporeal shock wave therapy in the
treatment of cubital tunnel syndrome. Methods: Seven patients (10 elbows) with moderate cubital
tunnel syndrome participated in this study. Three sessions of radial extracorporeal shock wave therapy
(2,000 shots, 4 Bar, 5 Hz) (once a week) were administered to the ulnar nerve at the proximal cubital
tunnel region. The primary and secondary outcomes were assessed using the Visual Analog Scale
(VAS) and the shortened Disabilities of the Arm, Shoulder and Hand questionnaire (Quick DASH),
respectively, at the 4th, 8th, and 12th week, following the 3rd session of shock wave therapy. Results:
The VAS and Quick DASH scores demonstrated improvements at all follow-up time points, in all
treated elbows. The mean VAS and Quick DASH score improved from 4.7±0.3(mean±SE) to2.2±0.2
and 16.6±2.1to 6.8±1.6 respectively during 12 weeks follow up (all p <0.01).
Conclusion: This pilot study revealed the safety and efficacy of extracorporeal shock wave therapy
in patients with moderate cubital tunnel syndrome.