Addressing Variability and Uncertainty in Plaque Rupture Prediction: The Role of Artificial Intelligence in Precision Cardiovascular Risk Assessment
DOI:
https://doi.org/10.58445/rars.2578Keywords:
Atherosclerosis, plaque rupture, artificial intelligence, machine learning, convolutional neural networks, cardiovascular risk prediction, real-time monitoring, personalized medicine, coronary artery disease, cardiovascular imagingAbstract
Plaque rupture within atherosclerotic arteries is the leading cause of acute cardiovascular events such as myocardial infarction and strokes. Despite recent advances in imaging and diagnostic techniques, current methods still lack precision to predict plaque rupture on an individual real time basis. They are also limited by their time-intensive nature and insufficient regard to dynamic physiological factors. Machine learning algorithms, specifically convolutional neural networks (CNNs), have the capability to enhance prediction accuracy, personalize risk assessments, and contribute to the development of real time monitoring systems. In this review ,I have outlined current challenges such as inter-patient variability, limitations in real time monitoring, gaps in current knowledge of plaque rupture mechanisms and how these can be addressed by artificial intelligence (AI). I also discuss current literature, identifies gaps in research, and propose future directions for integration of AI in cardiovascular treatment.
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