Background: Recent lifestyle changes have increased the prevalence of dyslipidemia in Korea. Young men are known to have a low awareness of dyslipidemia and a lack of motivation to maintain their weight. However, the association between weight change and dyslipidemia in young adults has not been thoroughly examined. Methods: Data from the Armed Forces Medical Command Defense Medical Information System database were used. In this study, 15,068 soldiers who underwent private and corporal health examinations between May 2020 and April 2022 were included. The difference in weights between the two different health examinations was used to quantify weight change. Four components of the lipid profile were used to assess dyslipidemia during the corporal health examination. Results: After adjusting for relevant covariates, weight gain was associated with increased risk of dyslipidemia (adjusted odds ratio [OR], 1.38 [95% confidence interval, CI, 1.15 to 1.64] for the 5% to 10% weight gain group;and OR, 2.02 [95% CI, 1.59 to 2.55] for the ≥10% weight gain group), whereas weight loss was associated with decreased risk (adjusted OR, 0.82 [95% CI, 0.68 to 0.98] for the 5% to 10% weight loss group; and OR, 0.38 [95% CI, 0.27 to 0.53] for the ≥10% weight loss group). In subgroup analysis based on the participants’ baseline body mass index, smoking status, regular exercise habits, and hypertension status, there were no significant differences between the subgroups. Conclusion Weight change was associated with dyslipidemia in Korean male soldiers. The findings suggest that limiting weight gain in young adults by encouraging a healthy lifestyle may help prevent dyslipidemia.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: Recent lifestyle changes have increased the prevalence of dyslipidemia in Korea. Young men are known to have a low awareness of dyslipidemia and a lack of motivation to maintain their weight. However, the association between weight change and dyslipidemia in young adults has not been thoroughly examined. Methods: Data from the Armed Forces Medical Command Defense Medical Information System database were used. In this study, 15,068 soldiers who underwent private and corporal health examinations between May 2020 and April 2022 were included. The difference in weights between the two different health examinations was used to quantify weight change. Four components of the lipid profile were used to assess dyslipidemia during the corporal health examination. Results: After adjusting for relevant covariates, weight gain was associated with increased risk of dyslipidemia (adjusted odds ratio [OR], 1.38 [95% confidence interval, CI, 1.15 to 1.64] for the 5% to 10% weight gain group;and OR, 2.02 [95% CI, 1.59 to 2.55] for the ≥10% weight gain group), whereas weight loss was associated with decreased risk (adjusted OR, 0.82 [95% CI, 0.68 to 0.98] for the 5% to 10% weight loss group; and OR, 0.38 [95% CI, 0.27 to 0.53] for the ≥10% weight loss group). In subgroup analysis based on the participants’ baseline body mass index, smoking status, regular exercise habits, and hypertension status, there were no significant differences between the subgroups. Conclusion Weight change was associated with dyslipidemia in Korean male soldiers. The findings suggest that limiting weight gain in young adults by encouraging a healthy lifestyle may help prevent dyslipidemia.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: Recent lifestyle changes have increased the prevalence of dyslipidemia in Korea. Young men are known to have a low awareness of dyslipidemia and a lack of motivation to maintain their weight. However, the association between weight change and dyslipidemia in young adults has not been thoroughly examined. Methods: Data from the Armed Forces Medical Command Defense Medical Information System database were used. In this study, 15,068 soldiers who underwent private and corporal health examinations between May 2020 and April 2022 were included. The difference in weights between the two different health examinations was used to quantify weight change. Four components of the lipid profile were used to assess dyslipidemia during the corporal health examination. Results: After adjusting for relevant covariates, weight gain was associated with increased risk of dyslipidemia (adjusted odds ratio [OR], 1.38 [95% confidence interval, CI, 1.15 to 1.64] for the 5% to 10% weight gain group;and OR, 2.02 [95% CI, 1.59 to 2.55] for the ≥10% weight gain group), whereas weight loss was associated with decreased risk (adjusted OR, 0.82 [95% CI, 0.68 to 0.98] for the 5% to 10% weight loss group; and OR, 0.38 [95% CI, 0.27 to 0.53] for the ≥10% weight loss group). In subgroup analysis based on the participants’ baseline body mass index, smoking status, regular exercise habits, and hypertension status, there were no significant differences between the subgroups. Conclusion Weight change was associated with dyslipidemia in Korean male soldiers. The findings suggest that limiting weight gain in young adults by encouraging a healthy lifestyle may help prevent dyslipidemia.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.