AIMC Topic: Cardiovascular Diseases

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Cardiovascular disease risk assessment through sensing the circulating microbiome with perovskite quantum dots leveraging deep learning models for bacterial species selection.

Mikrochimica acta
Perovskite quantum dots (PQDs) are novel nanomaterials wherein perovskites are used to formulate quantum dots (QDs). The present study utilizes the excellent fluorescence quantum yields of these nanomaterials to detect 16S rRNA of circulating microbi...

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Journal of the American College of Cardiology
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in diagnosis, treatment, and outcomes. Innovations to date focus on automating measurements, enhancing image quality, and detecting diseases using no...

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week.

Journal of the American College of Cardiology
Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% ...

Prediction of adverse cardiovascular events in children using artificial intelligence-based electrocardiogram.

International journal of cardiology
BACKGROUND: Convolutional neural networks (CNNs) have emerged as a novel method for evaluating heart failure (HF) in adult electrocardiograms (ECGs). However, such CNNs are not applicable to pediatric HF, where abnormal anatomy of congenital heart de...

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

Annals of internal medicine
BACKGROUND: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) recommend a risk calculator (ASCVD risk score) to estimate 10-year risk for major adverse cardiovascular events (MACE). Because the necessary inputs are o...

Prediction of cardiovascular and renal risk among patients with apparent treatment-resistant hypertension in the United States using machine learning methods.

Journal of clinical hypertension (Greenwich, Conn.)
Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment with three antihypertensives, is associated with an increased risk of developing cardiovascular and re...

Predictive analytics for cardiovascular patient readmission and mortality: An explainable approach.

Computers in biology and medicine
BACKGROUND: Cardiovascular patients experience high rates of adverse outcomes following discharge from hospital, which may be preventable through early identification and targeted action. This study aimed to investigate the effectiveness and explaina...

Deep learning of movement behavior profiles and their association with markers of cardiometabolic health.

BMC medical informatics and decision making
BACKGROUND: Traditionally, existing studies assessing the health associations of accelerometer-measured movement behaviors have been performed with few averaged values, mainly representing the duration of physical activities and sedentary behaviors. ...

Artificial intelligence in preventive cardiology.

Progress in cardiovascular diseases
Artificial intelligence (AI) is a field of study that strives to replicate aspects of human intelligence into machines. Preventive cardiology, a subspeciality of cardiovascular (CV) medicine, aims to target and mitigate known risk factors for CV dise...

BREATH-Net: a novel deep learning framework for NO prediction using bi-directional encoder with transformer.

Environmental monitoring and assessment
Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders...