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.

Background/Aims: We investigated the real-world effectiveness and safety of ustekinumab (UST) as induction treatment for Koreans with Crohn’s disease (CD). Methods: CD patients who started UST were prospectively enrolled from 4 hospitals in Korea. All enrolled patients received intravenous UST infusion at week 0 and subcutaneous UST injection at week 8. Clinical outcomes were assessed using Crohn’s Disease Activity Index (CDAI) scores at weeks 8 and 20 among patients with active disease (CDAI ≥150) at baseline. Clinical remission was defined as a CDAI <150, and clinical response was defined as a reduction in CDAI ≥70 points from baseline. Safety and factors associated with clinical remission at week 20 were also analyzed. Results: Sixty-five patients were enrolled between January 2019 and December 2020. Among 49 patients with active disease at baseline (CDAI ≥150), clinical remission and clinical response at week 8 were achieved in 26 (53.1%) and 30 (61.2%) patients, respectively. At week 20, 27 (55.1%) and 35 (71.4%) patients achieved clinical remission and clinical response, respectively. Twenty-seven patients (41.5%) experienced adverse events, with serious adverse events in 3 patients (4.6%). One patient (1.5%) stopped UST therapy due to poor response. Underweight (body mass index <18.5 kg/m2) (odds ratio [OR], 0.085; 95% confidence interval [CI], 0.014–0.498; P=0.006) and elevated C-reactive protein at baseline (OR, 0.133; 95% CI, 0.022–0.823; P=0.030) were inversely associated with clinical remission at week 20. Conclusions UST was effective and well-tolerated as induction therapy for Korean patients with CD.

Background: Adverse drug reactions (ADRs) are escalating, and their socioeconomic burden is increasing. However, large-scale prospective studies investigating ADRs during hospitalization are rare in Korea. We prospectively investigated the incidence, characteristics, and economic burden of ADRs in hospitalized patients based on electronic medical records (EMRs). Methods: Among patients admitted to three hospitals from October 2016 to October 2017, 5,000 patients were randomly selected and prospectively observed during hospitalization.Research nurses monitored and detected patients who had symptoms, signs, or laboratory findings suspicious for ADRs using an EMR-based detection protocol. Next, allergy and ADR specialists reviewed the medical records to determine the relationship between adverse reactions and drugs. Cases in which a causal relationship was certain, probable/likely, or possible were included in the ADR cases. Clinically meaningful ADR cases or those leading to prolonged hospitalization were defined as significant ADRs. Results: ADRs occurred in 510 (10.2%) patients. The mean length of hospital stay was approximately 5 days longer in patients with ADRs. Opioids accounted for the highest percentage of total ADRs. Significant ADRs were observed in 148 (3.0%) patients. Antibiotics accounted for the highest percentage of significant ADRs. Drug hypersensitivity reactions (DHRs) occurred in 88 (1.8%) patients. Antibiotics accounted for the highest percentage of DHRs. The average medical expenses for one day of hospitalization per patient were highest in significant ADRs, followed by non-significant ADRs, and non-ADRs. Conclusion ADRs in hospitalized patients are an important clinical issue, resulting in a substantial socioeconomic burden. EMR-based strategy could be a useful tool for ADR monitoring and early detection.

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

Objective:To construct training indexes of post competency of dental hygienist, and to provide objective basis for the establishment of the index system of the transition from dental nurse to dental hygienist in the future.Methods:The relevant literature of post competency of dental hygienist was searched from the databases such as PubMed, Medline, Web of Science, China National Knowledge Internet, Wanfang database. The search time was from the establishment of the database to March 2021. The expert letter inquiry questionnaire was designed through preliminary consultation.Delphi expert consultation was conducted for 20 dental experts from Beijing city, Chongqing city, Jiangsu province, Sichuan province, Jilin province from May to November 2021, and model indexes and weight assignment of post competency of dental hygienist were determined.Results:Three rounds of Delphi expert consultation were conducted, the effective recoveries of the questionnaires were 100%, the expert authority coefficients were 0.81, 0.81, 0.83, respectively, the coefficient of variation of expert consultation was 0.000-0.386, 0.000-0.300 and 0.000-0.250, respectively, the coordination degree of expert opinions in the third round of consultation was 0.679, 0.428 and 0.389 (all P<0.01). The formed training indexes of post competency of dental hygienist included 4 first-level indexes, 20 second-level indexes and 60 third-level indexes. Conclusions:The training index system of post competency of dental hygienist in this study is scientific, reliable, and practical, to provide reference for training and assessment of post competency of dental hygienist and objective basis for establishing the indexes system of dental nurse to dental hygienist in the future.