Background: Transthoracic echocardiography (TTE) requires time-intensive integration of quantitative measure‑ ments and qualitative visual assessment. Fully automated artificial intelligence (AI)-based analysis may reduce total analysis time while preserving accuracy, but systematic real-world validation remains limited. Methods: This prospective, single-center pilot study enrolled 40 TTE examinations. Identical deidentified DICOM datasets were independently provided to a trained cardiac sonographer and a fully automated AI system comprising quantitative and qualitative visual interpretation modules. All outputs were compared with a cardiologist-adjudicated reference standard. Primary endpoints were total analysis time and noninferiority of AI-derived left ventricular ejection fraction (LVEF) versus the reference standard, with a prespecified margin of 3 percentage points (one-sided α = 0.025). Results: Median analysis time was 94 s (interquartile range [IQR], 82–106 s) for the AI workflow versus 490 s (IQR, 438–626 s) for the human workflow (P < 0.001). AI-derived LVEF met the noninferiority criterion (mean difference, 0.00 percentage points; upper one-sided 95% confidence bound, 1.41 percentage points; P < 0.001), with an intraclass correlation coefficient (ICC) of 0.902 (95% confidence interval, 0.822–0.947). ICCs for secondary quantitative indi‑ ces ranged from 0.625 to 0.989. For aortic regurgitation severity grading, AI’s overall accuracy was 75.0% (quadratic weighted κ = 0.762), compared with 82.5% for human interpretation (κ = 0.812, McNemar P = 0.579). Conclusions Fully automated AI-assisted TTE analysis substantially reduced total analysis time while maintaining noninferior LVEF accuracy and acceptable performance across secondary quantitative and qualitative indices. These findings support the use of AI as a practical workflow accelerator in routine echocardiography.

Purpose: Patients experience severe physical trauma every year. However, studies on survivors’ experiences after severe injury are limited. Previous studies have mainly focused on time spans of trauma treatment. This study aimed to comprehensively explore survivors’ experiences to improve the current quality of trauma treatment and highlight the importance of patient-centered care. Materials and Methods: Structured, face-to-face interviews with six domains were conducted on survivors aged ≥18 years who were previously hospitalized in an intensive care unit due to traumatic injuries. Self-reported questionnaires were administered for a multidimensional assessment of participants’ conditions. Transcripts of each narrative were analyzed per grounded theory. Results: Fourteen participants were assessed. The median injury severity score was 25.5. The median elapsed time from injury to interview was 17.3 months. The physical and psychiatric difficulties of the participants remained unresolved even after completing rehabilitation. The main theme derived from the narratives were struggle with injury, consequences, and contributing factors, with the following subthemes: 1) suffering from injury and treatment, 2) psychological adaptation to the changed self and life after the accident, 3) significant family support, 4) gratitude to medical staff despite inadequacies in the healthcare system, and 5) legal and economic issues that impede recovery. Conclusion Increased efforts focusing on enabling survivors of severe injury to return to society and improve their quality of life are needed, including the establishment of patient-centered care in the trauma field, extended care for the survivors’ families, multidisciplinary treatment, and the collection of quantitative post-discharge data.

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.

Purpose: Patients experience severe physical trauma every year. However, studies on survivors’ experiences after severe injury are limited. Previous studies have mainly focused on time spans of trauma treatment. This study aimed to comprehensively explore survivors’ experiences to improve the current quality of trauma treatment and highlight the importance of patient-centered care. Materials and Methods: Structured, face-to-face interviews with six domains were conducted on survivors aged ≥18 years who were previously hospitalized in an intensive care unit due to traumatic injuries. Self-reported questionnaires were administered for a multidimensional assessment of participants’ conditions. Transcripts of each narrative were analyzed per grounded theory. Results: Fourteen participants were assessed. The median injury severity score was 25.5. The median elapsed time from injury to interview was 17.3 months. The physical and psychiatric difficulties of the participants remained unresolved even after completing rehabilitation. The main theme derived from the narratives were struggle with injury, consequences, and contributing factors, with the following subthemes: 1) suffering from injury and treatment, 2) psychological adaptation to the changed self and life after the accident, 3) significant family support, 4) gratitude to medical staff despite inadequacies in the healthcare system, and 5) legal and economic issues that impede recovery. Conclusion Increased efforts focusing on enabling survivors of severe injury to return to society and improve their quality of life are needed, including the establishment of patient-centered care in the trauma field, extended care for the survivors’ families, multidisciplinary treatment, and the collection of quantitative post-discharge data.

Purpose: Patients experience severe physical trauma every year. However, studies on survivors’ experiences after severe injury are limited. Previous studies have mainly focused on time spans of trauma treatment. This study aimed to comprehensively explore survivors’ experiences to improve the current quality of trauma treatment and highlight the importance of patient-centered care. Materials and Methods: Structured, face-to-face interviews with six domains were conducted on survivors aged ≥18 years who were previously hospitalized in an intensive care unit due to traumatic injuries. Self-reported questionnaires were administered for a multidimensional assessment of participants’ conditions. Transcripts of each narrative were analyzed per grounded theory. Results: Fourteen participants were assessed. The median injury severity score was 25.5. The median elapsed time from injury to interview was 17.3 months. The physical and psychiatric difficulties of the participants remained unresolved even after completing rehabilitation. The main theme derived from the narratives were struggle with injury, consequences, and contributing factors, with the following subthemes: 1) suffering from injury and treatment, 2) psychological adaptation to the changed self and life after the accident, 3) significant family support, 4) gratitude to medical staff despite inadequacies in the healthcare system, and 5) legal and economic issues that impede recovery. Conclusion Increased efforts focusing on enabling survivors of severe injury to return to society and improve their quality of life are needed, including the establishment of patient-centered care in the trauma field, extended care for the survivors’ families, multidisciplinary treatment, and the collection of quantitative post-discharge data.

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.

Purpose: Patients experience severe physical trauma every year. However, studies on survivors’ experiences after severe injury are limited. Previous studies have mainly focused on time spans of trauma treatment. This study aimed to comprehensively explore survivors’ experiences to improve the current quality of trauma treatment and highlight the importance of patient-centered care. Materials and Methods: Structured, face-to-face interviews with six domains were conducted on survivors aged ≥18 years who were previously hospitalized in an intensive care unit due to traumatic injuries. Self-reported questionnaires were administered for a multidimensional assessment of participants’ conditions. Transcripts of each narrative were analyzed per grounded theory. Results: Fourteen participants were assessed. The median injury severity score was 25.5. The median elapsed time from injury to interview was 17.3 months. The physical and psychiatric difficulties of the participants remained unresolved even after completing rehabilitation. The main theme derived from the narratives were struggle with injury, consequences, and contributing factors, with the following subthemes: 1) suffering from injury and treatment, 2) psychological adaptation to the changed self and life after the accident, 3) significant family support, 4) gratitude to medical staff despite inadequacies in the healthcare system, and 5) legal and economic issues that impede recovery. Conclusion Increased efforts focusing on enabling survivors of severe injury to return to society and improve their quality of life are needed, including the establishment of patient-centered care in the trauma field, extended care for the survivors’ families, multidisciplinary treatment, and the collection of quantitative post-discharge data.

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.