AIMC Topic: Heart Diseases
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Will artificial intelligence change the job of the cardiac imaging specialist?
Archives of cardiovascular diseases
Dec 30, 2019
Are All Deep Learning Architectures Alike for Point-of-Care Ultrasound?: Evidence From a Cardiac Image Classification Model Suggests Otherwise.
Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine
Dec 24, 2019
OBJECTIVES: Little is known about optimal deep learning (DL) approaches for point-of-care ultrasound (POCUS) applications. We compared 6 popular DL architectures for POCUS cardiac image classification to determine whether an optimal DL architecture e...
A New Era for Cyborg Science Is Emerging: The Promise of Cyborganic Beings.
Advanced healthcare materials
Nov 28, 2019
Living flesh, hacked beyond known biological borders, and sophisticated machineries, made by humans, are currently being united to address some of the impending challenges in medicine. Imagine biological systems made from smart biomaterials with the ...
Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning.
Artificial intelligence in medicine
Nov 8, 2019
In today's world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can...
Identification and Quantification of Cardiovascular Structures From CCTA: An End-to-End, Rapid, Pixel-Wise, Deep-Learning Method.
JACC. Cardiovascular imaging
Oct 11, 2019
OBJECTIVES: This study designed and evaluated an end-to-end deep learning solution for cardiac segmentation and quantification.
Synthesis of Electrocardiogram V-Lead Signals From Limb-Lead Measurement Using R-Peak Aligned Generative Adversarial Network.
IEEE journal of biomedical and health informatics
Aug 21, 2019
Recently, portable electrocardiogram (ECG) hardware devices have been developed using limb-lead measurements. However, portable ECGs provide insufficient ECG information because of limitations in the number of leads and measurement positions. Therefo...
Neural-network classification of cardiac disease from P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 12, 2019
BACKGROUND: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive...
Phosphocreatine
Phosphorus Isotopes
Adenosine Triphosphate
Heart Diseases
Energy Metabolism
Magnetic Resonance Spectroscopy
Humans
Adult
Male
Reproducibility of Results
Middle Aged
Aged
Severity of Illness Index
Aged, 80 and over
Predictive Value of Tests
Young Adult
Myocardium
Neural Networks, Computer
Creatine Kinase
Machine Learning
Kinetics
Biomarkers
Female
The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis.
International journal of environmental research and public health
Jul 29, 2019
The applications of artificial intelligence (AI) in aiding clinical decision-making and management of stroke and heart diseases have become increasingly common in recent years, thanks in part to technological advancements and the heightened interest ...
Prediction of good neurological recovery after out-of-hospital cardiac arrest: A machine learning analysis.
Resuscitation
Jul 27, 2019
BACKGROUND: This study aimed to train, validate and compare predictive models that use machine learning analysis for good neurological recovery in OHCA patients.
Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function.
JACC. Cardiovascular imaging
Jul 17, 2019
OBJECTIVES: This study sought to develop a fully automated framework for cardiac function analysis from cardiac magnetic resonance (CMR), including comprehensive quality control (QC) algorithms to detect erroneous output.
Automation
Humans
Magnetic Resonance Imaging, Cine
Female
Ventricular Function, Right
Reproducibility of Results
Quality Control
Heart Diseases
Image Interpretation, Computer-Assisted
Middle Aged
Quality Indicators, Health Care
Diagnosis, Computer-Assisted
Aged
Deep Learning
Predictive Value of Tests
Male
Ventricular Function, Left
Stroke Volume
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