AI Medical Compendium Topic

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Cardiovascular System

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Blood Biomarkers Predict Cardiac Workload Using Machine Learning.

BioMed research international
INTRODUCTION: Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure product (rRPP) varies from one individual to the next, but its biochemical/cellular phenotype remains ...

Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.

Computers in biology and medicine
Zebrafish is a powerful and widely-used model system for a host of biological investigations, including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quant...

Analyses of child cardiometabolic phenotype following assisted reproductive technologies using a pragmatic trial emulation approach.

Nature communications
Assisted reproductive technologies (ART) are increasingly used, however little is known about the long-term health of ART-conceived offspring. Weak selection of comparison groups and poorly characterized mechanisms impede current understanding. In a ...

What will we ask to artificial intelligence for cardiovascular medicine in the next decade?

Minerva cardiology and angiology
Artificial intelligence (AI) comprises a wide range of technologies and methods with heterogeneous degrees of complexity, applications, and abilities. In the cardiovascular field, AI holds the potential to fulfil many unsolved challenges, eventually ...

Artificial Intelligence and Cardiac PET/Computed Tomography Imaging.

PET clinics
Artificial intelligence is an important technology, with rapidly expanding applications for cardiac PET. We review the common terminology, including methods for training and testing, which are fundamental to understanding artificial intelligence. Nex...

Machine Learning in Cardiovascular Imaging.

Heart failure clinics
The number of cardiovascular imaging studies is growing exponentially, and so is the demand to improve the efficacy of the imaging workflow. Over the past decade, studies have demonstrated that machine learning (ML) holds promise to revolutionize car...

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.

Annals of biomedical engineering
Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown si...

Highlights of the Virtual Society for Cardiovascular Magnetic Resonance 2022 Scientific Conference: CMR: improving cardiovascular care around the world.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
The 25th Society for Cardiovascular Magnetic Resonance (SCMR) Annual Scientific Sessions saw 1524 registered participants from more than 50 countries attending the meeting virtually. Supporting the theme "CMR: Improving Cardiovascular Care Around the...

Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

European heart journal
Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to imp...