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.

Objectives: Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. Methods: The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. Results: Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. Conclusions Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.

Objective: This phase IV, multicenter, randomized controlled, open-label, and parallel clinical trial aimed to compare the efficacy and safety of ezetimibe and moderate intensity rosuvastatin combination therapy to that of high intensity rosuvastatin monotherapy in patients with atherosclerotic cardiovascular disease (ASCVD). Methods: This study enrolled patients with ASCVD and after a four-week screening period, patients were randomly assigned to receive either rosuvastatin and ezetimibe (RE 10/10 group) or high-intensity rosuvastatin (R20 group) only in a 1:1 ratio. The primary outcome was the difference in the percent change in the mean low-density lipoprotein cholesterol (LDL-C) level from baseline to 12 weeks between two groups after treatment. Results: The study found that after 12 and 24 weeks of treatment, the RE10/10 group had a greater reduction in LDL-C level compared to the R20 group (−22.9±2.6% vs. −15.6 ± 2.5% [p=0.041] and −24.2±2.5% vs. −12.9±2.4% [p=0.001] at 12 and 24 weeks, respectively). Moreover, a greater number of patients achieved the target LDL-C level of ≤70 mg/dL after the treatment period in the combination group (74.6% vs. 59.9% [p=0.012] and 76.2% vs. 50.8% [p<0.001] at 12 and 24 weeks, respectively). Importantly, there were no significant differences in the occurrence of overall adverse events and adverse drug reactions between two groups. Conclusion Moderate-intensity rosuvastatin and ezetimibe combination therapy had better efficacy in lowering LDL-C levels without increasing adverse effects in patients with ASCVD than high-intensity rosuvastatin monotherapy.

Objectives: E-detailing methods have steadily evolved toward more contactless and interactive channels, which have received considerable attention during the coronavirus disease 2019 (COVID-19) crisis. Based on the technology acceptance model, this study attempted to identify medical representatives’ perceptions and attitudes towards individual innovativeness that affected users’ intentions to adopt new e-detailing devices utilizing information and communication technology. Methods: The subjects of the current study were medical representatives at three major multinational or domestic pharmaceutical companies that operate in South Korea. In total, 300 questionnaires were distributed and 221 were returned. The survey elicited information on respondents’ perceived ease of use (PEOU), perceived usefulness (PU), personal innovativeness (PI), and user acceptance (UA) of remote e-detailing technology, in addition to demographic information and occupational characteristics. Structural equation models were fitted to the data. Separate analyses were conducted for different platform types, PCs and mobile devices. Results: PEOU showed a statistically significant positive association with PU. PEOU, PU, and PI were associated with UA, and PI was a statistically significant moderator. On average, PEOU explained up to approximately 45% of the total variation in UA of remote e-detailing. Conclusions The analysis supports the framework of the technology acceptance model. PEOU was a substantially strong direct predictor of UA, and PI had a statistically significant, positive moderating effect between PU and UA. Medical representatives with pro-innovative attitudes are more likely to play the role of early adopters of remote e-detailing if they find this technology to be more useful.

Objectives: The prevalence of psychiatric disorders among young adults is different from that among younger or older age groups because of biological and environmental changes. The purpose of this study was to analyze the diagnostic distribution of psychiatric disorders in 19–30-year-old Koreans based on their age and gender using data from the Korean National Health Insurance Service (NHIS). Methods: From the 2011 medical claims sample data of NHIS of 1,375,842 people, we extracted the data of 221,038 people aged 19–30 years, including 106,232 (48.1%) men and 114,806 (51.9%) women. We evaluated the overall changes in the diagnostic distribution of psychiatric disorders over a 3-year period. Results: The diagnostic frequency in women was 13,627 (59.0%), which was significantly higher than that in men. “Other anxiety disorders” was the most common psychiatric disorder in both genders, followed by depressive episodes, somatoform disorders, “other neurotic disorders,” and nonorganic sleep disorders. In men, attention-deficit/hyperactivity disorder or intellectual disabilities were not among the top 10 disorders. In women, no significant changes in major psychiatric disorders were seen over the 3-year period. Conclusion These results reveal the trends of diagnostic distribution of mental illnesses depending on the development, particularly in young adulthood. It is necessary to identify whether such trends are due to biological or environmental factors, aging processes, or complex influences.

Objectives: The prevalence of psychiatric disorders among young adults is different from that among younger or older age groups because of biological and environmental changes. The purpose of this study was to analyze the diagnostic distribution of psychiatric disorders in 19–30-year-old Koreans based on their age and gender using data from the Korean National Health Insurance Service (NHIS). Methods: From the 2011 medical claims sample data of NHIS of 1,375,842 people, we extracted the data of 221,038 people aged 19–30 years, including 106,232 (48.1%) men and 114,806 (51.9%) women. We evaluated the overall changes in the diagnostic distribution of psychiatric disorders over a 3-year period. Results: The diagnostic frequency in women was 13,627 (59.0%), which was significantly higher than that in men. “Other anxiety disorders” was the most common psychiatric disorder in both genders, followed by depressive episodes, somatoform disorders, “other neurotic disorders,” and nonorganic sleep disorders. In men, attention-deficit/hyperactivity disorder or intellectual disabilities were not among the top 10 disorders. In women, no significant changes in major psychiatric disorders were seen over the 3-year period. Conclusion These results reveal the trends of diagnostic distribution of mental illnesses depending on the development, particularly in young adulthood. It is necessary to identify whether such trends are due to biological or environmental factors, aging processes, or complex influences.