Latest AI and machine learning research in myocardial infarction for healthcare professionals.
BACKGROUND: A 48-year-old man with a coronary artery calcium (CAC) score of 0 underwent serial artificial intelligence (AI)-assisted coronary computed tomography angiography (CCTA) from 2015 to 2026, which revealed progressive noncalcified plaque. FIRST-IN-HUMAN/EARLY REPORTS SUMMARY: Despite the absence of baseline calcification, serial imaging demonstrated increasing plaque volume before initiat...
BACKGROUND: Chronic total occlusion (CTO) interventions are frequently limited by incomplete angiographic information. We report the use of a novel artificial intelligence (AI)-based spatiotemporal enhancement processing (STEP) software (AngioWave Imaging), which identified both an antegrade crossing pathway and an epicardial collateral, neither of which was apparent on the unprocessed angiogram. ...
INTRODUCTION: Transthoracic echocardiography (TTE) is the current standard for detecting tricuspid regurgitation (TR); however, it incurs additional c...
AIMS: Existing ST-segment elevation myocardial infarction (STEMI) alert pathways that rely on traditional STEMI criteria perform suboptimally. We aime...
Transthyretin amyloid cardiomyopathy (ATTR-CM) and aortic stenosis (AS) frequently coexist in elderly patients, particularly men, creating a complex c...
AIMS: Artificial intelligence models can estimate a person's age from ECG. The gap between the predicted ECG age and chronological age, predicted age ...
BACKGROUND: Predicting the origin of premature ventricular contractions (PVCs) is challenging when a transition zone (TZ) appears in leads V3 and V4. ...
BACKGROUND: The identification of anomalies in physiological time-series data, specifically ECG and EEG spectra, is a key part of the diagnostic proce...
Sleep stage flagging is critical for diagnosing conditions like insomnia, sleep apnea, and narcolepsy. Traditional methods rely on time-intensive manu...
Artificial intelligence (AI) can augment coronary angiography images to enhance interpretation. We compared two blinded operators' interpretation of c...
BACKGROUND: Non-linear equine electrocardiography (ECG) analysis is an actively developing study area which has the potential to lead to novel, artifi...
The growing number of cancer cases and deaths highlights the urgent need for innovative treatment approaches. One technique that has lately been recog...
Exome sequencing (ES) has transformed genomic research and clinical diagnostics by enabling precise identification of disease-associated variants with...
AIMS: To develop and evaluate a deep learning model for immediate and accurate diagnosis of acute heart failure(HF) using standard 12-lead electrocard...
BACKGROUND: Artificial intelligence applied to electrocardiograms (ECG-AI) offers a scalable approach to identify individuals at risk for heart failur...
Digital twin technology, which enables the creation of patient-specific virtual models, is increasingly applied in interventional cardiology to suppor...
BACKGROUND: Up to 50% of patients presenting with ST-elevation myocardial infarction (STEMI) have multivessel coronary artery disease (CAD). Randomize...
BACKGROUND AND OBJECTIVE: Artificial Intelligence (AI) models for electrocardiogram (ECG) interpretation rely on large, diverse datasets, but existing...
Recent debates have focused on the impact of analytical imprecision in high-sensitivity cardiac troponin (hs-cTnI and hs-cTnT) assays on the diagnosis...