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: The purpose of this study was to detect signals of adverse events (AEs) of DPP-IV inhibitors using the KIDs-Korea Adverse Event Reporting System (KAERS) database. Methods: This study was conducted using AEs reported from January 2009to December 2018 in the KIDs-KAERS database. For signal detection, disproportionality analysis was performed. Signals of DPPIV inhibitor that satisfied the data-mining indices of reporting odds ratio (ROR) were detected. Results: Among the total number of 10,364 AEs to all oral hypoglycemic agents, the number of reported AEs related to DPP-IV inhibitors was 1,674. Analysis of re-ported AEs of DPP-IV inhibitors at the SOC levels showed that Respiratory system disorders were the highest at 4.31 (95% CI 3.01-6.17), followed by Skin and appendages disorders at 2.04 (95% CI 1.74-2.38). When analyzing AEs reported at the PT level, phar-yngitis was the highest at 73.90 (95% CI 17.59-310.49), followed by arthralgia at 6.08 (95% CI 2.04-18.11), and coughing at 5.21 (95% CI 2.07-13.15). Conclusions Based on the result of the study, deeper consideration is required according to the characteristics of the patients in prescribing DPP-IV inhibitors among oral hypoglycemic agents, and continuous monitoring of the occurrence of related Adverse Drug Reactions during administration is also required.

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: The purpose of this study was to detect signals of adverse events (AEs) of DPP-IV inhibitors using the KIDs-Korea Adverse Event Reporting System (KAERS) database. Methods: This study was conducted using AEs reported from January 2009to December 2018 in the KIDs-KAERS database. For signal detection, disproportionality analysis was performed. Signals of DPPIV inhibitor that satisfied the data-mining indices of reporting odds ratio (ROR) were detected. Results: Among the total number of 10,364 AEs to all oral hypoglycemic agents, the number of reported AEs related to DPP-IV inhibitors was 1,674. Analysis of re-ported AEs of DPP-IV inhibitors at the SOC levels showed that Respiratory system disorders were the highest at 4.31 (95% CI 3.01-6.17), followed by Skin and appendages disorders at 2.04 (95% CI 1.74-2.38). When analyzing AEs reported at the PT level, phar-yngitis was the highest at 73.90 (95% CI 17.59-310.49), followed by arthralgia at 6.08 (95% CI 2.04-18.11), and coughing at 5.21 (95% CI 2.07-13.15). Conclusions Based on the result of the study, deeper consideration is required according to the characteristics of the patients in prescribing DPP-IV inhibitors among oral hypoglycemic agents, and continuous monitoring of the occurrence of related Adverse Drug Reactions during administration is also required.

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: The purpose of this study was to detect signals of adverse events (AEs) of DPP-IV inhibitors using the KIDs-Korea Adverse Event Reporting System (KAERS) database. Methods: This study was conducted using AEs reported from January 2009to December 2018 in the KIDs-KAERS database. For signal detection, disproportionality analysis was performed. Signals of DPPIV inhibitor that satisfied the data-mining indices of reporting odds ratio (ROR) were detected. Results: Among the total number of 10,364 AEs to all oral hypoglycemic agents, the number of reported AEs related to DPP-IV inhibitors was 1,674. Analysis of re-ported AEs of DPP-IV inhibitors at the SOC levels showed that Respiratory system disorders were the highest at 4.31 (95% CI 3.01-6.17), followed by Skin and appendages disorders at 2.04 (95% CI 1.74-2.38). When analyzing AEs reported at the PT level, phar-yngitis was the highest at 73.90 (95% CI 17.59-310.49), followed by arthralgia at 6.08 (95% CI 2.04-18.11), and coughing at 5.21 (95% CI 2.07-13.15). Conclusions Based on the result of the study, deeper consideration is required according to the characteristics of the patients in prescribing DPP-IV inhibitors among oral hypoglycemic agents, and continuous monitoring of the occurrence of related Adverse Drug Reactions during administration is also required.

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: The purpose of this study was to detect signals of adverse events (AEs) of DPP-IV inhibitors using the KIDs-Korea Adverse Event Reporting System (KAERS) database. Methods: This study was conducted using AEs reported from January 2009to December 2018 in the KIDs-KAERS database. For signal detection, disproportionality analysis was performed. Signals of DPPIV inhibitor that satisfied the data-mining indices of reporting odds ratio (ROR) were detected. Results: Among the total number of 10,364 AEs to all oral hypoglycemic agents, the number of reported AEs related to DPP-IV inhibitors was 1,674. Analysis of re-ported AEs of DPP-IV inhibitors at the SOC levels showed that Respiratory system disorders were the highest at 4.31 (95% CI 3.01-6.17), followed by Skin and appendages disorders at 2.04 (95% CI 1.74-2.38). When analyzing AEs reported at the PT level, phar-yngitis was the highest at 73.90 (95% CI 17.59-310.49), followed by arthralgia at 6.08 (95% CI 2.04-18.11), and coughing at 5.21 (95% CI 2.07-13.15). Conclusions Based on the result of the study, deeper consideration is required according to the characteristics of the patients in prescribing DPP-IV inhibitors among oral hypoglycemic agents, and continuous monitoring of the occurrence of related Adverse Drug Reactions during administration is also required.

Purpose: High-dose radiotherapy (RT) for localized prostate cancer requires careful consideration of target position changes and adjacent organs-at-risk (OARs), such as the rectum and bladder. Therefore, daily monitoring of target position and OAR changes is crucial in minimizing interfractional dosimetric uncertainties. For efficient monitoring of the internal condition of patients, we assessed the feasibility of an auto-segmentation of OARs on the daily acquired images, such as megavoltage computed tomography (MVCT), via a commercial artificial intelligence (AI)-based solution in this study. Materials and Methods: We collected MVCT images weekly during the entire course of RT for 100 prostate cancer patients treated with the helical TomoTherapy system. Based on the manually contoured body outline, the bladder including prostate area, and rectal balloon regions for the 100 MVCT images, we trained the commercially available fully convolutional (FC)-DenseNet model and tested its auto-contouring performance. Results: Based on the optimally determined hyperparameters, the FC-DenseNet model successfully auto-contoured all regions of interest showing high dice similarity coefficient (DSC) over 0.8 and a small mean surface distance (MSD) within 1.43 mm in reference to the manually contoured data. With this well-trained AI model, we have efficiently monitored the patient's internal condition through six MVCT scans, analyzing DSC, MSD, centroid, and volume differences. Conclusion We have verified the feasibility of utilizing a commercial AI-based model for auto-segmentation with low-quality daily MVCT images. In the future, we will establish a fast and accurate auto-segmentation and internal organ monitoring system for efficiently determining the time for adaptive replanning.

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.