AIMC Topic: Hemodynamics

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A 2-year investigation of the impact of the computed tomography-derived fractional flow reserve calculated using a deep learning algorithm on routine decision-making for coronary artery disease management.

European radiology Feb 25, 2021
OBJECTIVE: This study aims to investigate the safety and feasibility of using a deep learning algorithm to calculate computed tomography angiography-based fractional flow reserve (DL-FFRCT) as an alternative to invasive coronary angiography (ICA) in ...
Retrospective Studies Tomography, X-Ray Computed Severity of Illness Index Hemodynamics Humans Algorithms Coronary Artery Disease Computed Tomography Angiography Coronary Stenosis Fractional Flow Reserve, Myocardial Deep Learning Predictive Value of Tests Coronary Angiography
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Is Deep Reinforcement Learning Ready for Practical Applications in Healthcare? A Sensitivity Analysis of Duel-DDQN for Hemodynamic Management in Sepsis Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Jan 25, 2021
The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it...
Sepsis Reinforcement, Psychology Humans Algorithms Learning Delivery of Health Care Hemodynamics Deep Learning
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Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning.

Communications biology Jan 22, 2021
The clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-spec...
Coronary Artery Bypass Humans Hemodynamics Deep Learning
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Neural network-based modeling of the number of microbubbles generated with four circulation factors in cardiopulmonary bypass.

Scientific reports Jan 12, 2021
The need for the estimation of the number of microbubbles (MBs) in cardiopulmonary bypass surgery has been recognized among surgeons to avoid postoperative neurological complications. MBs that exceed the diameter of human capillaries may cause endoth...
Capillaries Hemodynamics Cardiopulmonary Bypass Nervous System Diseases Cardiovascular Surgical Procedures Blood Viscosity Neural Networks, Computer Humans Microbubbles Postoperative Complications Embolism, Air
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Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology Dec 30, 2020
OBJECTIVE: To classify children with autism spectrum disorder (ASD) and typical development (TD) using short-term spontaneous hemodynamic fluctuations and to explore the abnormality of inferior frontal gyrus and temporal lobe in ASD.
Humans Deep Learning Child Frontal Lobe Temporal Lobe Autism Spectrum Disorder Spectroscopy, Near-Infrared Hemodynamics
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Multi-band MR fingerprinting (MRF) ASL imaging using artificial-neural-network trained with high-fidelity experimental data.

Magnetic resonance in medicine Oct 26, 2020
PURPOSE: We aim to leverage the power of deep-learning with high-fidelity training data to improve the reliability and processing speed of hemodynamic mapping with MR fingerprinting (MRF) arterial spin labeling (ASL).
Data Compression Moyamoya Disease Arteries Gray Matter Cerebrovascular Circulation Spin Labels Male Hemodynamics Magnetic Resonance Imaging Perfusion Healthy Volunteers Brain Mapping Kinetics Female Reproducibility of Results Young Adult Adult Humans Neural Networks, Computer Computer Simulation
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Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning.

Scientific reports Sep 14, 2020
Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardiac contractility is still challenging. This paper investigated the potential of estimating aortic sys...
Humans Machine Learning Models, Cardiovascular Hemodynamics Heart Myocardial Contraction Aorta
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Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks.

Scientific reports Jun 11, 2020
Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient's hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors ...
Kinetics Models, Biological Aorta Neural Networks, Computer Hemodynamics Constriction, Pathologic
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A pilot study using a machine-learning approach of morphological and hemodynamic parameters for predicting aneurysms enhancement.

International journal of computer assisted radiology and surgery Jun 8, 2020
PURPOSE: The development of straightforward classification methods is needed to identify unstable aneurysms and rupture risk for clinical use. In this study, we aim to investigate the relative importance of geometrical, hemodynamic and clinical risk ...
Female Linear Models Middle Aged Male Stress, Mechanical Aneurysm, Ruptured Humans Hemodynamics Intracranial Aneurysm Machine Learning Pilot Projects
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Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes.

Circulation. Cardiovascular imaging May 18, 2020
BACKGROUND: There is a lack of studies investigating the heterogeneity of patients with aortic stenosis (AS). We explored whether cluster analysis identifies distinct subgroups with different prognostic significances in AS.
Cause of Death Aortic Valve Aged Unsupervised Machine Learning Aged, 80 and over Predictive Value of Tests Male Echocardiography Humans Female Prospective Studies Decision Support Techniques Hemodynamics Reproducibility of Results Phenotype Prognosis Middle Aged Cluster Analysis Age Factors Risk Factors Comorbidity Aortic Valve Stenosis Severity of Illness Index
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