AIMC Topic: Diastole
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Cardiac phase detection in echocardiography using convolutional neural networks.
Scientific reports
Jun 1, 2023
Echocardiography is a commonly used and cost-effective test to assess heart conditions. During the test, cardiologists and technicians observe two cardiac phases-end-systolic (ES) and end-diastolic (ED)-which are critical for calculating heart chambe...
A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Feb 27, 2023
BACKGROUND: Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac shape models, however, limits th...
End-systole and end-diastole detection in short axis cine MRI using a fully convolutional neural network with dilated convolutions.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 3, 2022
The correct assessment and characterization of heart anatomy and functionality is usually done through inspection of magnetic resonance image cine sequences. In the clinical setting it is especially important to determine the state of the left ventri...
Can Machine Learning Help Simplify the Measurement of Diastolic Function in Echocardiography?
JACC. Cardiovascular imaging
Jul 14, 2021
Multibeat echocardiographic phase detection using deep neural networks.
Computers in biology and medicine
Apr 6, 2021
BACKGROUND: Accurate identification of end-diastolic and end-systolic frames in echocardiographic cine loops is important, yet challenging, for human experts. Manual frame selection is subject to uncertainty, affecting crucial clinical measurements, ...
Artificial Intelligence and Left Ventricular Diastolic Function Assessment: A New Tool for Improved Practice?
Journal of cardiothoracic and vascular anesthesia
Feb 16, 2021
Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nov 24, 2020
Electrocardiogram (ECG) is often used together with a spectral Doppler ultrasound to separate heart cycles by determining the end-diastole locations. However, the ECG signal is not always recorded. In such cases, the cardiac cycles can be estimated m...
Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning.
BMC cardiovascular disorders
Aug 14, 2020
BACKGROUND: Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to iden...
Ventricular Dysfunction, Left
Natriuretic Peptide, Brain
Asymptomatic Diseases
Diastole
Humans
Biomarkers
Predictive Value of Tests
Retrospective Studies
Peptide Fragments
Heart Failure
Deep Learning
Stroke Volume
Cluster Analysis
Risk Assessment
Echocardiography
Female
Aged, 80 and over
Unsupervised Machine Learning
Disease Progression
Middle Aged
Risk Factors
Male
Diagnosis, Computer-Assisted
Aged
Ventricular Function, Left
Using machine learning to characterize heart failure across the scales.
Biomechanics and modeling in mechanobiology
Jun 25, 2019
Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the p...
Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates.
Sensors (Basel, Switzerland)
May 8, 2019
Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP's normality based on oscillometric measurements, we use statistical...
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