A proposed deep learning system aims to analyze coronary angiography images to detect irregular arteries and predict cardiovascular events in patients with no obstructive lesions.
Can a deep learning-based coronary angiography image analysis system detect irregular coronary arteries and predict cardiovascular events in patients with no obstructive coronary lesions?
A proposed deep learning system aims to analyze coronary angiograms of patients without obstructive lesions to characterize arteries and predict future cardiovascular events.
The emergence of deep learning has caused its massive application to different fields in industry and research, among which is the clinical field, especially in those where the data is structured in the form of images or video. The present proposal intends to develop a coronary angiography image analysis system based on artificial intelligence. These images are radiocontrast X-ray images of the coronary arteries. The proposed system will be able to analyze these coronary angiography images of patients with no obstructive coronary lesions to detect and characterize smooth and irregular coronary arteries and predict the presence of cardiovascular events during follow-up. Deep learning convolutional artificial neural networks will support the algorithmic basis of the proposed system.
Luque‐Baena et al. (Wed,) conducted a other in Coronary stenosis. Deep learning convolutional artificial neural networks was evaluated on Detection and characterization of smooth and irregular coronary arteries and prediction of cardiovascular events. A proposed deep learning system aims to analyze coronary angiography images to detect irregular arteries and predict cardiovascular events in patients with no obstructive lesions.
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