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.

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.

OBJECTIVES: The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review. METHODS: We first established a concept and definition of “hospitalization episode,” taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms’ accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm- identified events. RESULTS: We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively. CONCLUSIONS We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.

OBJECTIVE: To retrospectively analyze the clinical efficacy of anterior cruciate ligament (ACL) reconstruction combined with anterolateral complex repair and ACL reconstruction combined with ALL reconstruction in the treatment of anterior cruciate ligament injuries with high-grade pivot shift. METHODS: From January 2018 to June 2022, 49 patients combined ACL and ALL injuries with high-grade pivot shift were retrospectively studied from three hospitals, 29 of them underwent ACL reconstruction with anterolateral complex repair (repair group), including 23 males and 6 females with an average age of (27.5±4.8) years old, ranged from 20 to 37 years old;the injured sides were 13 on the left and 16 on the right, and 11 patients were suffered with meniscus injury. The other 20 patients underwent ACL and ALL reconstruction (reconstruction group) including 17 males and 3 females with the mean age of (27.1±4.5) years old, ranged from 20 to 38 years old;the injured sides were 8 on the left and 12 on the right, and 6 patients were suffered with meniscus injury. Knee stability (pivot shift test, KT-2000), range of motion, knee function (Lysholm scoring scale, Cincinnati sports activity scale (CSAS) scoring scale, and Tegner activity level score between two groups were compared. RESULTS: A total of 49 patients were followed up, the repair group receiving 13 to 20(15.3±1.8) months and the reconstruction group receiving 12 to 21(16.0±2.2) months. There was no statistically significant difference in the preoperative pivot shift test grading distribution between two groups (P>0.05). At the last postoperative follow-up, there were 24 patients with grade 0 and 5 patients with grade 1 in the repair group, and there were 18 patients with grade 0 and 2 patients with grade 1 in the reconstruction group, there is no significant difference in the distribution of axial shift test grading between two groups(P>0.05). The preoperative KT-2000 tibial displacement of two groups were (9.39±0.77) mm (repair group) and (9.14±0.78) mm (reconstruction group) respectively, with no statistically significant difference (P>0.05). At the final postoperative follow-up, there were 24 patients with KT-2000 tibial displacement <3 mm and 5 patients with 3 to 5 mm in the repair group, while 18 patients with <3 mm and 2 patients with 3 to 5 mm in the reconstruction group, KT-2000 tibial displacement distribution of two groups was no significant difference (P>0.05), but the KT-2000 tibial displacement in the reconstruction group (1.30±0.86) mm was significantly smaller than that in the repair group (1.99±1.11) mm (P<0.05). The final postoperative follow-up range of motion of the contralateral side knee between two groups was no significant difference (P>0.05). The range of motion of the suffering knee in the repair group was less than that in the reconstruction group (P<0.05). There was no significant difference in preoperative Lysholm and CSAS scores between two groups (P>0.05). At the final postoperative follow-up, both groups showed significant improvement in Lysholm and CSAS scores, while the Lysholm and CSAS scores of the reconstruction group were better than those of the repair group, and the difference was statistically significant (P<0.05). Significant differences was found in Tegner scores between two groups, which 16 patients in the repair group returned to their pre-injury activity level, and 17 patients in the reconstruction group returned to their pre-injury level (P<0.05). CONCLUSION Compared to anterolateral complex repair, combined ACL and ALL reconstruction in the treatment of ACL injuries with high-grade pivot shift results in better knee joint function and stability. This is advantageous in reducing the risk of ACL reconstruction failure.
Humans ; Male ; Female ; Adult ; Anterior Cruciate Ligament Reconstruction/methods* ; Anterior Cruciate Ligament Injuries/surgery* ; Young Adult ; Retrospective Studies ; Anterior Cruciate Ligament/surgery* ; Range of Motion, Articular

Complicated chemical reactions occur in the decoction of traditional Chinese medicines(TCMs) which features complex components, influencing the safety, efficacy, and quality controllability of TCMs. Therefore, it is particularly important to clarify the chemical reaction mechanism of TCMs in the decoction. This study summarized eight typical chemical reactions in the decoction of TCMs, such as substitution reaction, redox reaction, isomerization/stereoselective reaction, complexation, and supramolecular reaction. With the "toxicity attenuation and efficiency enhancement" of aconitines and other examples, this study reviewed the reactions in decoction of TCMs, which was expected to clarify the variation mechanisms of key chemical components in this process and to help guide medicine preparation and safe and rational use of medicine in clinical settings. The current main research methods for chemical reaction mechanisms of decoction of TCMs were also summed up and compared. The novel real-time analysis device of decoction system for TCMs was found to be efficient and simple without the pre-treatment of samples. This device provides a promising solution, which has great potential in quantity evaluation and control of TCMs. Moreover, it is expected to become a foundational and exemplary research tool, which can advance the research in this field.
Medicine ; Medicine, Chinese Traditional ; Research Design

Based on the development of conditions, the etiology and pathogenesis of jingjin (muscle region of meridian) diseases are summarized as 3 stages, i.e. stagnation due to over-exertion at early stage, manifested by tendon-muscle contracture and tenderness; cold condition due to stagnation, interaction of stasis and cold, resulting in clustered nodules at the middle stage; prolonged illness and missed/delayed treatment, leading to tendon-muscle contracture and impairment of joint function at the late stage. It is proposed that the treatment of jingjin diseases should be combined with the characteristic advantages of fire needling and bloodletting technique, on the base of "eliminating stagnation and bloodletting/fire needling". This combined therapy warming yang to resolve stasis and dispels cold to remove nodules, in which, eliminating the stagnation is conductive to the tissue regeneration, and the staging treatment is delivered in terms of the condition development at different phases.
Acupuncture Therapy/methods* ; Bloodletting ; Medicine, Chinese Traditional ; Muscular Diseases/therapy* ; Humans ; Hot Temperature/therapeutic use* ; Contracture/therapy*