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 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: A considerable amount of research has shown that knowledge and appropriate awareness are essential for encouraging positive behaviors and promoting health. In Korea, the roles that behavioral changes play in the prevention of cancer have been an important issue since the introduction of the 10 codes for cancer prevention in 2006. Thus, the present study investigated the associations of tobacco-related knowledge with awareness and attitudes towards positive smoking-cessation behaviors. METHODS: The present study analyzed data from the 2010 national questionnaire survey (n = 1,006). This study evaluated sociodemographic characteristics, smoking status, self-rated health status, health-related interests, and the accuracy of 12 tobacco-related statements to determine knowledge level and to investigate its impact on awareness and behaviors related to smoking. These parameters were examined and staged using the Precaution Adoption Process Model. RESULTS: A higher level of tobacco-related knowledge was significantly associated with a positive attitude towards smoking cessation (5–8 correct answers: odds ratio [OR], 2.53; 95% confidence interval [CI], 1.57–4.08; ≥ 9 correct answers: OR, 3.90; 95% CI, 2.22–6.82; reference: ≤ 4 correct answers). Interestingly, among current smokers, only those who correctly responded to ≥ 9 of 12 tobacco-related statements were significantly associated with a positive attitude towards smoking cessation. CONCLUSION: This study found that having a higher level of tobacco-related knowledge had a significant impact on positive attitudes towards smoking cessation. This suggests that there is a need to disseminate appropriate knowledge to the general population to encourage positive attitudes and promote healthful behaviors in terms of smoking.
Korea ; Odds Ratio ; Smoke* ; Smoking Cessation ; Smoking*

PURPOSE: Human papillomavirus (HPV) vaccination has been included in the National Immunization Program in Korea since 2016. We aimed to evaluate changes in the awareness of and attitudes toward HPV vaccination, among adults in Korea since the first introduction of the vaccines in 2007. MATERIALS AND METHODS: A nationwide population-based survey was conducted in 2016 for 1,200 nationally representative Korean men and women; the data obtained were compared with the data from the nationwide survey conducted in 2007. RESULTS: A significant increase in the awareness of HPV infection (35.8%) and vaccination (36.9%) was observed in 2016 from 13.3% and 8.6% in 2007, respectively. Willingness to be vaccinated against HPV decreased from 55.0% in 2007 to 25.8% in 2016, and the proportion of respondents expressing uncertainty increased from 28.3% in 2007 to 43.3% in 2016. Only 12.1% of men and 22.0% of women knew about the free national HPV vaccination program for girls, launched in June 2016. Younger women, with higher income level, awareness of the HPV vaccine, and perception of the seriousness of infections had a higher willingness to be vaccinated. A high education level, awareness of HPV infection and vaccination, and perception of the seriousness of infection were positively associated with the willingness of respondents to vaccinate their daughters. CONCLUSION: Raising the awareness of HPV infection and vaccination with appropriate knowledge is necessary for the successful implementation of the national HPV vaccination program.
Adult* ; Education ; Female ; Humans* ; Immunization Programs ; Korea* ; Male ; Nuclear Family ; Surveys and Questionnaires ; Uncertainty ; Vaccination* ; Vaccines

Coronary magnetic resonance angiography (CMRA) allows a noninvasive assessment of the coronary anatomy without exposing the patients to radiation. It is also superior to coronary computed tomography angiography (CCTA) for the evaluation of luminal narrowing in heavily calcified coronary segments. We report a case with triple-vessel disease, but it could not be accurately assessed by CCTA because of calcification and lack of a significant perfusion defect or myocardial scarring on cardiac magnetic resonance imaging (MRI). However, whole-heart CMRA performed as part of the cardiac MRI protocol demonstrated significant triple-vessel disease with left main involvement, confirmed by subsequent invasive angiography with a fractional flow reserve measurement.
Angiography ; Cicatrix ; Coronary Artery Disease* ; Coronary Vessels* ; Humans ; Magnetic Resonance Angiography* ; Magnetic Resonance Imaging ; Perfusion ; Phenobarbital

The proton magnetic resonance spectroscopy (¹H-MRS) is a tool used to detect concentrations of brain metabolites such as N-acetyl aspartate, choline, creatine, glutamate, and gamma-amino butyric acid (GABA). It has been widely used because it does not require additional devices other than the conventional magnetic resonance scanner and coils. Demyelination, or the neuronal damage due to loss of myelin sheath, is one of the common pathologic processes in many diseases including multiple sclerosis, leukodystrophy, encephalomyelitis, and other forms of autoimmune diseases. Rodent models mimicking human demyelinating diseases have been induced by using virus (e.g., Theiler's murine encephalomyelitis virus) or toxins (e.g., cuprizon or lysophosphatidyl choline). This review is an overview of the MRS findings on brain metabolites in demyelination with a specific focus on rodent models.
Animals* ; Aspartic Acid ; Autoimmune Diseases ; Brain ; Butyric Acid ; Choline ; Creatine ; Demyelinating Diseases* ; Encephalomyelitis ; Glutamic Acid ; Humans ; Models, Animal* ; Multiple Sclerosis ; Myelin Sheath ; Neurons ; Pathologic Processes ; Proton Magnetic Resonance Spectroscopy ; Rodentia ; Spectrum Analysis*

PURPOSE: In some countries with high smoking prevalence, smoke-free legislation has only been implemented in specific public places, as opposed to a comprehensive ban on smoking in all public places. The purpose of this study was to provide valid data on second-hand smoke (SHS) exposure that reflect the consequences of incomplete smoke-free legislation, and provide a rationale for expanding this legislation. MATERIALS AND METHODS: Indoor and outdoor environmental exposure (fine particulate matter [PM2.5], air nicotine, and dust 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone [NNK]) was monitored in 35 public places where smoking is prohibited by law in Goyang, Republic of Korea. Biomarkers of SHS exposure (urinary cotinine, hair nicotine, and urinary 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol) were measured in 37 non-smoking employees. Geometric means and standard deviations were used in comparison of each measure. RESULTS: Considerable exposure of SHS was detected at all indoor monitoring sites (PM2.5, 95.5 mug/m3 in private educational institutions; air nicotine, 0.77 mug/m3 in large buildings; and dust NNK, 160.3 pg/mg in large buildings); environmental measures were higher in private or closed locations, such as restrooms. Outdoor measures of SHS exposure were lowest in nurseries and highest in government buildings. Biochemical measures revealed a pattern of SHS exposure by monitoring site, and were highest in private educational institutions. CONCLUSION: The evidence of SHS exposure in legislative smoke-free places in Korea suggests that incomplete smoke free legislation and lack of enforcement of it might not protect people from exposure to smoke. Therefore, active steps should be taken toward a comprehensive ban on smoking in all public places and its enforcement.
Biological Markers ; Cotinine ; Dust ; Environmental Exposure ; Environmental Health ; Hair ; Health Policy ; Jurisprudence ; Korea ; Nicotine ; Nurseries ; Particulate Matter ; Prevalence ; Republic of Korea* ; Smoke ; Smoking ; Tobacco Smoke Pollution

PURPOSE: The objective of this study was to measure secondhand smoke (SHS) exposure in personal computer (PC) rooms with the purpose of determining the strength of scientific evidence supporting the legislative ban on smoking in PC rooms located in the Republic of Korea. MATERIALS AND METHODS: From June to September 2012, particulate matter (PM2.5) and air nicotine concentration (ANC) were measured in the smoking and non-smoking areas of PC rooms in Goyang City, Korea. In 28 randomly sampled PC rooms, field investigators completed an observational questionnaire on building characteristics, smoking policies, and evidence of smoking. The geometric means (GM) of PM2.5 and ANC in smoking and non-smoking areas were compared. RESULTS: Evidence of smoking was identified in both the smoking and non-smoking areas of all PC rooms. The GMs of PM2.5 and ANC in both areas were high and did not differ significantly (174.77 μg/m3 and 48.95 μg/m3 in smoking areas; 93.38 μg/m3 and 41.30 μg/m3 in non-smoking areas). Overall PM2.5 concentrations were 5.5-fold higher than those listed in the World Health Organization guidelines. CONCLUSION: This study supported previous reports that a partial smoking ban did not protect individuals from SHS exposure. Furthermore, the results from our study suggest how research can support policy. Countries in which smoke-free policies are not yet comprehensive may find our results useful.
Humans ; Korea* ; Microcomputers ; Nicotine ; Particulate Matter ; Republic of Korea ; Research Personnel ; Smoke ; Smoke-Free Policy ; Smoking ; Tobacco Smoke Pollution* ; World Health Organization