Key result
Machine learning techniques, particularly deep learning, offer promise to accelerate flow modeling, enhance blood flow imaging resolution, and accurately detect cardiovascular diseases.
Why the study?
Computational cost, limited spatiotemporal resolution, and data analysis obstacles have hindered emerging modeling, imaging, and wearable sensing techniques in cardiovascular diseases.
Machine learning and deep learning techniques hold significant potential to overcome current limitations in computational fluid dynamics, blood flow imaging, and wearable sensing for cardiovascular disease management.
Should not yet change cardiovascular imaging practice; leaves open need for prospective validation of deep learning applications.
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.
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Moradi et al. (2023) conducted a review in Cardiovascular diseases. Machine learning techniques was evaluated. Machine learning techniques, particularly deep learning, offer promise to accelerate flow modeling, enhance blood flow imaging resolution, and accurately detect cardiovascular diseases.
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