The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural
networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer …

Deep learning and computer vision algorithms can deliver highly accurate and automated
interpretation of medical imaging to augment and assist clinicians. However, medical …

While cardiovascular diseases (CVDs) are commonly diagnosed by cardiologists via
inspecting electrocardiogram (ECG) waveforms, these decisions can be supported by a data …

Echocardiography uses ultrasound technology to capture high temporal and spatial
resolution images of the heart and surrounding structures, and is the most common imaging …

Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach
to automated medical image diagnosis. Recently, vision transformers (ViTs) have appeared …

Cardiovascular imaging technologies continue to increase in their capacity to capture and
store large quantities of data. Modern computational methods, developed in the field of …

Introduction: Non-invasive imaging plays a critical role in managing patients with
cardiovascular disease. Although subjective visual interpretation remains the clinical …

Objectives Many deep learning-based predictive models evaluate the waveforms of
electrocardiograms (ECGs). Because deep learning-based models are data-driven, large …

Background Laboratory testing is routinely used to assay blood biomarkers to provide
information on physiologic state beyond what clinicians can evaluate from interpreting …

Abstract Aims Deep neural networks (DNNs) perform excellently in interpreting
electrocardiograms (ECGs), both for conventional ECG interpretation and for novel …