Why the study?
Accurate measurement of left ventricular volume from CMR images is critical for cardiovascular disease prediction but requires improved data processing methods.
p-value: p=<0.001
A deep learning approach using convolutional neural networks was proposed for measuring left ventricular volume from CMR images, though results remain preliminary.
Cardiovascular disease prediction is very critical area of research.In this work we tried to measure the left ventricular volume which plays an important role in cardiac arrests.In this work we contributed in data pre-processing of the CMR images then applied deep neural network.The data used in this work was Sunnybrook Cardiac Dataset (SCD) and Cardiac Atlas Project (CAP).It is LV CMR images dataset.Convolution neural network and maxpooling with ADAM activation function was applied for the proposed architecture.The results are at very initial stages and further enhancement could be done in the future by applying more efficient pre-processing techniques.
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Zakariah et al. (2020) studied Cardiovascular disease (n=140). Deep convolutional neural network vs. Ground truth was evaluated on Left ventricular volume estimation (ESV, EDV, EF) (p=<0.001).
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