Single View Echocardiographic Analysis for LVOT Obstruction Prediction in Hypertrophic Cardiomyopathy: A Deep Learning Approach.

Background: Accurate left ventricular outflow tract obstruction (LVOTO) assessment is crucial for hypertrophic cardiomyopathy (HCM) management and prognosis. Traditional methods, requiring multiple views, Doppler, and provocation, is often infeasible, especially where resources are limited. This study aimed to develop and validate a deep learning (DL) model capable of predicting severe LVOTO in HCM patients using only the parasternal long-axis (PLAX) view from transthoracic echocardiography (TTE).

Influence of Rhythm Control on Functional Mitral and Tricuspid Regurgitation Severity and Clinical Outcome in Patients With Atrial Fibrillation: A Propensity-Matched Cohort Study.

Background And Objectives: This study evaluated the efficacy of atrial fibrillation (AF) rhythm control therapy in improving functional mitral regurgitation (MR) and tricuspid regurgitation (TR) and its association with clinical outcomes.

Methods: Among 2,574 patients with AF screened from 2003 to 2023, 817 pairs of patients were selected through propensity matching to compare rhythm control therapy (antiarrhythmic drugs, catheter ablation, or electrical cardioversion) with no rhythm control. MR and TR severity were assessed at baseline and follow-up echocardiography conducted at least 3-month intervals.

On-treatment blood pressure and dose-dependent effects of ARNI in heart failure with reduced ejection fraction: Insights from a multicenter registry.

Background: Achieving target doses of angiotensin receptor-neprilysin inhibitor (ARNI) in heart failure with reduced ejection fraction (HFrEF) is often challenging due to concerns related to hypotension. This study evaluated dose-dependent effects of ARNI considering on-treatment blood pressure (BP).

Methods: From a multicenter HF registry, 1,097 HFrEF patients receiving ARNI for ≥6 months were stratified into low-dose (<100 mg/day, n = 249) and intermediate-to-high-dose (≥100 mg/day, n = 848) groups.

Novel deep learning framework for simultaneous assessment of left ventricular mass and longitudinal strain: clinical feasibility and validation in patients with hypertrophic cardiomyopathy.

Background: This study aims to present the Segmentation-based Myocardial Advanced Refinement Tracking (SMART) system, a novel artificial intelligence (AI)-based framework for transthoracic echocardiography (TTE) that incorporates motion tracking and left ventricular (LV) myocardial segmentation for automated LV mass (LVM) and global longitudinal strain (LVGLS) assessment.

Methods: The SMART system demonstrates LV speckle tracking based on motion vector estimation, refined by structural information using endocardial and epicardial segmentation throughout the cardiac cycle. This approach enables automated measurement of LVM and LVGLS.

Real-World Experience of Mavacamten for Patients With Obstructive Hypertrophic Cardiomyopathy in South Korea: A Prospective Multi-Center Observational Study.

Background And Objectives: Mavacamten has shown promise in obstructive hypertrophic cardiomyopathy (oHCM); however, real-world evidence is limited in Asians. We aimed to provide the first multicenter, real-world analysis of mavacamten use in Korea.

Methods: This prospective observational study included symptomatic oHCM patients treated at 7 tertiary hospitals in Korea.

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

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).

Left Atrial Strain Predicts Poor Exercise Capacity in Patients With Indeterminate Diastolic Function.

Background And Objectives: The 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging guidelines report that approximately 20% of diastolic dysfunction is indeterminate and has limited diagnostic accuracy. Left atrial strain may help accurately categorize diastolic dysfunction; however, its exact roles remain unclear. This study investigated the impact of left atrial reservoir strain (LARS) and its association with exercise capacity in patients with indeterminate diastolic function.

Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.

Background: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.

Methods: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation.

Automated extracellular volume fraction measurement for diagnosis and prognostication in patients with light-chain cardiac amyloidosis.

Aims: T1 mapping on cardiac magnetic resonance (CMR) imaging is useful for diagnosis and prognostication in patients with light-chain cardiac amyloidosis (AL-CA). We conducted this study to evaluate the performance of T1 mapping parameters, derived from artificial intelligence (AI)-automated segmentation, for detection of cardiac amyloidosis (CA) in patients with left ventricular hypertrophy (LVH) and their prognostic values in patients with AL-CA.

Methods And Results: A total of 300 consecutive patients who underwent CMR for differential diagnosis of LVH were analyzed.

Cardiopulmonary exercise test with bicycle stress echocardiography for predicting adverse cardiac events in patients with stage A or B heart failure.

Background: Given the high prevalence of stage A or B heart failure (HF), comprehensive screening for new-onset HF is cost-prohibitive. Therefore, further risk stratification is warranted to identify at-risk patients. This study aimed to evaluate the prognostic utility of cardiopulmonary exercise test (CPET) with bicycle stress echocardiography (BSE) in patients with stage A or B HF.

An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI.

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.

Impact of changes in left heart geometry on predicting new-onset atrial fibrillation in patients with hypertension.

Background: Hypertension-induced left ventricular hypertrophy (LVH) increases end-diastolic LV pressure and contributes to left atrial enlargement (LAE), which are associated with development of atrial fibrillation. However, the impact of LVH and LAE and their regression following antihypertensive therapy on atrial fibrillation incidence remains unclear.

Methods: This retrospective analysis included consecutive patients with sinus rhythm who underwent echocardiography at hypertension diagnosis and after 6-18 months between 2006 and 2021 at tertiary care centres in Korea.

Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction.

Background: Evaluating left ventricular diastolic function (LVDF) is crucial in echocardiography; however, the complexity and time demands of current guidelines challenge clinical use. This study aimed to develop an artificial intelligence (AI)-based framework for automatic LVDF assessment to reduce subjectivity and improve accuracy and outcome prediction.

Methods: We developed an AI-based LVDF assessment framework using a nationwide echocardiographic dataset from five tertiary hospitals.

Performance of ECG-Derived Digital Biomarker for Screening Coronary Occlusion in Resuscitated Out-of-Hospital Cardiac Arrest Patients: A Comparative Study between Artificial Intelligence and a Group of Experts.

Acute coronary syndrome is a significant part of cardiac etiology contributing to out-of-hospital cardiac arrest (OHCA), and immediate coronary angiography has been proposed to improve survival. This study evaluated the effectiveness of an AI algorithm in diagnosing near-total or total occlusion of coronary arteries in OHCA patients who regained spontaneous circulation. Conducted from 1 July 2019 to 30 June 2022 at a tertiary university hospital emergency department, it involved 82 OHCA patients, with 58 qualifying after exclusions.

Integrative Assessment of Cardiopulmonary Fitness Using Cardiopulmonary Exercise Test With Supine Bicycle Echocardiography in Patients Presenting Dyspnea.

Background: Cardiopulmonary exercise test (CPET) with supine bicycle echocardiography (SBE) enables comprehensive physiologic assessment during exercise. We characterized cardiopulmonary fitness by integrating CPET-SBE parameters and evaluated its prognostic value in patients presenting with dyspnea.

Methods And Results: We retrospectively reviewed 473 consecutive patients who underwent CPET-SBE for dyspnea evaluation.

Age-dependent implications of left ventricular hypertrophy regression in patients with hypertension.

Left ventricular hypertrophy (LVH) is a significant risk factor for cardiovascular mortality and morbidity in patients with hypertension. However, the effect of age on LVH regression or persistence and its differential prognostic value remain unclear. Therefore, we investigated the clinical implications of LVH regression in 1847 patients with hypertension and echocardiography data (at baseline and during antihypertensive treatment at an interval of 6-18 months) according to age.

Clinical and Vessel Characteristics Associated With Hard Outcomes After PCI and Their Combined Prognostic Implications.

Background Cardiac death or myocardial infarction still occurs in patients undergoing contemporary percutaneous coronary intervention (PCI). We aimed to identify adverse clinical and vessel characteristics related to hard outcomes after PCI and to investigate their individual and combined prognostic implications. Methods and Results From an individual patient data meta-analysis of 17 cohorts of patients who underwent post-PCI fractional flow reserve measurement after drug-eluting stent implantation, 2081 patients with available clinical and vessel characteristics were analyzed.

Endocardial versus whole-myocardial tracking global longitudinal strain analysis in patients with hypertrophic cardiomyopathy: A preliminary comparative study.

Background And Objectives: We investigated whether the feasibility of left ventricular (LV) global longitudinal strain (GLS) in hypertrophic cardiomyopathy (HCM) varies according to the methodology (e.g. endocardial vs.

Feasibility of Artificial Intelligence-Based Electrocardiography Analysis for the Prediction of Obstructive Coronary Artery Disease in Patients With Stable Angina: Validation Study.

Background: Despite accumulating research on artificial intelligence-based electrocardiography (ECG) algorithms for predicting acute coronary syndrome (ACS), their application in stable angina is not well evaluated.

Objective: We evaluated the utility of an existing artificial intelligence-based quantitative electrocardiography (QCG) analyzer in stable angina and developed a new ECG biomarker more suitable for stable angina.

Methods: This single-center study comprised consecutive patients with stable angina.