Background and Objectives: Voice disorders can significantly impact quality of life. This study evaluates the feasibility of using deep learning models to detect voice disorders using an opensource dataset.Materials and Method We utilized the Saarbrücken Voice Database, which contains 1231 voice recordings of various pathologies. Datasets were used for training (n=1036) and validation (n=195). Key vocal parameters, including fundamental frequency (F0), formants (F1, F2), harmonics-to-noise ratio, jitter, and shimmer, were analyzed. A convolutional neural network (CNN) was designed to classify voice recordings into normal, vox senilis, and laryngocele. Performance was assessed using precision, recall, F1-score, and accuracy. Results: The CNN model demonstrated high classification performance, with precision, recall, and F1-scores of 1.00 for normal and 0.99 for vox senilis and laryngocele. Accuracy reached 1.00 after 50 epochs and remained stable through 100 epochs. Time-frequency analysis supported the model’s ability to differentiate between classes. Conclusion This study highlights the potential of deep learning for voice disorder detection, achieving high accuracy and precision. Future research should address dataset diversity and realworld integration for broader clinical adoption.

Background/Aims: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted global health, exacerbated metabolic health issues, and altered lifestyle behaviors. This study examined the sex-specific impact of the COVID-19 outbreak on the incidence of metabolic syndrome using data from the Korea National Health and Nutrition Examination Survey (KNHANES). Methods: Data from the KNHANES VII (2018) and VIII (2019–2021), including 15,499 participants, were analyzed. The study population was stratified by sex, and further subdivisions were conducted based on the timeframe relative to the COVID-19 outbreak. Variables such as age, education level, household income, smoking status, and high-risk drinking were analyzed to assess their influence on the prevalence of metabolic syndrome. Results: The overall prevalence of metabolic syndrome significantly increased from 28.11% before the outbreak to 29.69% after the outbreak. Both males and females reported significant increases in waist circumference and fasting glucose levels. Age and education level differentially influenced the prevalence of metabolic syndrome between the sex. Smoking was significantly associated with increased prevalence in males, whereas high-risk drinking was associated with increased prevalence in males and decreased prevalence in females. Conclusions The COVID-19 pandemic has significantly increased the prevalence of metabolic syndrome with notable sex-specific differences. These findings highlight the need for sex-specific public health interventions to mitigate the impact of the pandemic on metabolic health.

Background: Computed tomography (CT) scans are utilized to assess emphysema, a prominent phenotype of chronic obstructive pulmonary disease (COPD). Variability in CT protocols and equipment across hospitals can impact accuracy. This study aims to implement kernel conversion across different CT settings and evaluate changes in the correlation between the emphysema index pre- and post-kernel conversion, along with clinical measures in COPD patients. Methods: Data were extracted from the Korea COPD Subgroup Study database, which included CT scan images from 484 COPD patients. These images underwent kernel conversion. Emphysema extent was quantified using the percentage of low-attenuation areas (%LAA-950) determined by a deep learning-based program. The correlation between %LAA-950 and clinical parameters, including lung function tests, the modified Medical Research Council (mMRC), 6-minute walking distance (6MWD), COPD assessment test (CAT), and the St. George’s Respiratory Questionnaire for COPD (SGRQ-c), was analyzed. Subsequently, these values were compared across various CT settings. Results: A total of 484 participants were included. Kernel conversion significantly reduced the variance in %LAA-950 values (before vs. after: 12.6±11.0 vs. 8.8±11.9). Post-kernel conversion, %LAA-950 demonstrated moderate correlations with forced expiratory volume in 1 second (r=–0.41), residual volume/total lung capacity (r=0.42), mMRC (r=0.25), CAT score (r=0.12), SGRQ-c (r=0.21), and 6MWD (r=0.15), all of which were improved compared to the unconverted dataset (all p<0.01). Conclusion CT images processed through kernel conversion enhance the correlation between the extent of emphysema and clinical parameters in COPD.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Background: Complete revascularization has demonstrated better outcomes in patients with acute myocardial infarction (AMI) and multivessel disease. However, in the case of left main (LM) culprit lesion AMI with multivessel disease, there is limited evidence to suggest that complete revascularization is better. Methods: We reviewed 16,831 patients in the Korea Acute Myocardial Infarction Registry who were treated from July 2016 to June 2020, and 399 patients were enrolled with LM culprit lesion AMI treated with percutaneous coronary intervention. We categorized the patients as those treated with complete revascularization (n=295) or incomplete revascularization (n=104). The study endpoint was major adverse cardiac and cerebrovascular events (MACCE), a composite of all-cause death, myocardial infarction, ischemia-driven revascularization, stent thrombosis, and stroke. We performed propensity score matching (PSM) and analyzed the incidence of MACCE at 1 year. Results: After PSM, the two groups were well balanced. There was no significant difference between the two groups in MACCE at 1 year (12.1% vs. 15.2%; hazard ratio, 1.28; 95% confidence interval, 0.60–2.74; p=0.524) after PSM. The components of MACCE and major bleeding were also not significantly different. Conclusion There was no significant difference in clinical outcomes between the groups treated with complete or incomplete revascularization for LM culprit lesion AMI with multivessel disease.

Background/Aims: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted global health, exacerbated metabolic health issues, and altered lifestyle behaviors. This study examined the sex-specific impact of the COVID-19 outbreak on the incidence of metabolic syndrome using data from the Korea National Health and Nutrition Examination Survey (KNHANES). Methods: Data from the KNHANES VII (2018) and VIII (2019–2021), including 15,499 participants, were analyzed. The study population was stratified by sex, and further subdivisions were conducted based on the timeframe relative to the COVID-19 outbreak. Variables such as age, education level, household income, smoking status, and high-risk drinking were analyzed to assess their influence on the prevalence of metabolic syndrome. Results: The overall prevalence of metabolic syndrome significantly increased from 28.11% before the outbreak to 29.69% after the outbreak. Both males and females reported significant increases in waist circumference and fasting glucose levels. Age and education level differentially influenced the prevalence of metabolic syndrome between the sex. Smoking was significantly associated with increased prevalence in males, whereas high-risk drinking was associated with increased prevalence in males and decreased prevalence in females. Conclusions The COVID-19 pandemic has significantly increased the prevalence of metabolic syndrome with notable sex-specific differences. These findings highlight the need for sex-specific public health interventions to mitigate the impact of the pandemic on metabolic health.

Background: Computed tomography (CT) scans are utilized to assess emphysema, a prominent phenotype of chronic obstructive pulmonary disease (COPD). Variability in CT protocols and equipment across hospitals can impact accuracy. This study aims to implement kernel conversion across different CT settings and evaluate changes in the correlation between the emphysema index pre- and post-kernel conversion, along with clinical measures in COPD patients. Methods: Data were extracted from the Korea COPD Subgroup Study database, which included CT scan images from 484 COPD patients. These images underwent kernel conversion. Emphysema extent was quantified using the percentage of low-attenuation areas (%LAA-950) determined by a deep learning-based program. The correlation between %LAA-950 and clinical parameters, including lung function tests, the modified Medical Research Council (mMRC), 6-minute walking distance (6MWD), COPD assessment test (CAT), and the St. George’s Respiratory Questionnaire for COPD (SGRQ-c), was analyzed. Subsequently, these values were compared across various CT settings. Results: A total of 484 participants were included. Kernel conversion significantly reduced the variance in %LAA-950 values (before vs. after: 12.6±11.0 vs. 8.8±11.9). Post-kernel conversion, %LAA-950 demonstrated moderate correlations with forced expiratory volume in 1 second (r=–0.41), residual volume/total lung capacity (r=0.42), mMRC (r=0.25), CAT score (r=0.12), SGRQ-c (r=0.21), and 6MWD (r=0.15), all of which were improved compared to the unconverted dataset (all p<0.01). Conclusion CT images processed through kernel conversion enhance the correlation between the extent of emphysema and clinical parameters in COPD.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Background/Aims: The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted global health, exacerbated metabolic health issues, and altered lifestyle behaviors. This study examined the sex-specific impact of the COVID-19 outbreak on the incidence of metabolic syndrome using data from the Korea National Health and Nutrition Examination Survey (KNHANES). Methods: Data from the KNHANES VII (2018) and VIII (2019–2021), including 15,499 participants, were analyzed. The study population was stratified by sex, and further subdivisions were conducted based on the timeframe relative to the COVID-19 outbreak. Variables such as age, education level, household income, smoking status, and high-risk drinking were analyzed to assess their influence on the prevalence of metabolic syndrome. Results: The overall prevalence of metabolic syndrome significantly increased from 28.11% before the outbreak to 29.69% after the outbreak. Both males and females reported significant increases in waist circumference and fasting glucose levels. Age and education level differentially influenced the prevalence of metabolic syndrome between the sex. Smoking was significantly associated with increased prevalence in males, whereas high-risk drinking was associated with increased prevalence in males and decreased prevalence in females. Conclusions The COVID-19 pandemic has significantly increased the prevalence of metabolic syndrome with notable sex-specific differences. These findings highlight the need for sex-specific public health interventions to mitigate the impact of the pandemic on metabolic health.

Background: Computed tomography (CT) scans are utilized to assess emphysema, a prominent phenotype of chronic obstructive pulmonary disease (COPD). Variability in CT protocols and equipment across hospitals can impact accuracy. This study aims to implement kernel conversion across different CT settings and evaluate changes in the correlation between the emphysema index pre- and post-kernel conversion, along with clinical measures in COPD patients. Methods: Data were extracted from the Korea COPD Subgroup Study database, which included CT scan images from 484 COPD patients. These images underwent kernel conversion. Emphysema extent was quantified using the percentage of low-attenuation areas (%LAA-950) determined by a deep learning-based program. The correlation between %LAA-950 and clinical parameters, including lung function tests, the modified Medical Research Council (mMRC), 6-minute walking distance (6MWD), COPD assessment test (CAT), and the St. George’s Respiratory Questionnaire for COPD (SGRQ-c), was analyzed. Subsequently, these values were compared across various CT settings. Results: A total of 484 participants were included. Kernel conversion significantly reduced the variance in %LAA-950 values (before vs. after: 12.6±11.0 vs. 8.8±11.9). Post-kernel conversion, %LAA-950 demonstrated moderate correlations with forced expiratory volume in 1 second (r=–0.41), residual volume/total lung capacity (r=0.42), mMRC (r=0.25), CAT score (r=0.12), SGRQ-c (r=0.21), and 6MWD (r=0.15), all of which were improved compared to the unconverted dataset (all p<0.01). Conclusion CT images processed through kernel conversion enhance the correlation between the extent of emphysema and clinical parameters in COPD.