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Diastole

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Using machine learning to characterize heart failure across the scales.

Biomechanics and modeling in mechanobiology
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...

Estimation of End-Diastole in Cardiac Spectral Doppler Using Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
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...

Multibeat echocardiographic phase detection using deep neural networks.

Computers in biology and medicine
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, ...

Estimation of left ventricular parameters based on deep learning method.

Mathematical biosciences and engineering : MBE
Estimating material properties of personalized human left ventricular (LV) modelling is a central problem in biomechanical studies. In this work we use deep learning (DL) method to evaluating the passive myocardial mechanical properties inversely. In...

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
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...

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
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...