This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS. Materials and Methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05. Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS. Conclusion A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

Background and Objectives: Hypertrophic cardiomyopathy (HCM) increases the risk of sudden cardiac death. Despite international guidelines for sudden death prevention and implantable cardiac defibrillator (ICD) use, clinical practices vary. This study assessed the practices and opinions of Korean cardiologists on HCM management, focusing on ICD use and reimbursement policies. Methods: A structured electronic survey was distributed to members of the Korean Society of Cardiology, covering HCM management, ICD preferences, and reimbursement policies. Results: A total of 119 cardiologists participated, with most being arrhythmia specialists (56.3%) and university professors (81.5%). Cardiac magnetic resonance imaging and genetic testing were underutilized (30.3% and 11.8%, respectively). Beta-blocker-refractory premature ventricular complexes were primarily managed with continued medical therapy (77.3%). ICD preferences varied by physician specialty and patient age, with subcutaneous ICDs preferred in younger patients and dual-chamber ICDs in older patients. Nearly half of respondents believed ICD reimbursement criteria required revision. Conclusions Significant variability in HCM management exists among Korean cardiologists, influenced by restrictive reimbursement criteria rather than clinical guidelines. Updated reimbursement policies and improved access to advanced diagnostics are needed to align with international standards and optimize patient care.