Tricuspid regurgitation (TR) remains underdiagnosed due to the lack of effective screening tools. We developed a self-distilled and pre-trained transformer model for detecting TR (SPEED-TR) from electrocardiography. The model was trained using 466,149 electrocardiogram-echocardiogram…
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Heart rate (HR) estimation is crucial for early cardiovascular diagnosis, continuous monitoring, and various health applications. While electrocardiography (ECG) remains the gold standard, its discomfort and impracticality for continuous use have spurred the development…
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What You Should Know:
– Mayo Clinic is expanding its collaboration with SandboxAQ, a provider of artificial intelligence (AI) medical apps to explore the use of electrocardiography (ECG) and magnetocardiography (MCG) technologies for…
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Deep learning analysis of electrocardiography (ECG) may predict cardiovascular outcomes. We present a novel multi-task deep learning model, the ECG-MACE, which predicts the one-year first-ever major adverse cardiovascular events (MACE) using 2,821,889 standard 12-lead…
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The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950…
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Researchers at the University of Texas at Austin have developed a new chest wearable that can obtain both electrocardiogram and seismocardiogram data from the underlying heart. While basic ECG can be monitored via smart…
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