Advancements in Artificial Intelligence and Machine Learning Algorithms for Enhancing Disease Progression Prediction through Cardiovascular Imaging: Review

Abstract

Background: Cardiovascular diseases (CVDs) are the leading cause of death globally, accounting for approximately 31% of all fatalities. Timely and accurate diagnosis through cardiovascular imaging is crucial for effective patient management. However, traditional imaging methods often face challenges, including inter-observer variability and prolonged processing times.
Methods: This review explores the integration of artificial intelligence (AI) and machine learning (ML) in cardiovascular imaging, focusing on their applications across various modalities such as echocardiography, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear imaging. A comprehensive literature search was conducted to identify studies showcasing AI and ML advancements in predicting disease progression and enhancing diagnostic accuracy.
Results: The findings demonstrate that AI and ML algorithms significantly improve diagnostic precision and efficiency. For example, convolutional neural networks (CNNs) have been successfully employed in automated image classification and segmentation, yielding high accuracy in assessing conditions like coronary artery disease and valvular heart disorders. The application of AI in echocardiography reduced image analysis time by 77% compared to traditional methods, while deep learning techniques in CT enhanced the detection of coronary artery stenosis and plaque characterization.
Conclusion: The implementation of AI and ML in cardiovascular imaging presents substantial opportunities for enhancing diagnostic capabilities and patient outcomes. Despite the promising advancements, challenges such as data quality, model interpretability, and ethical considerations must be addressed to ensure safe integration into clinical practice. Future research should focus on optimizing these technologies for personalized medicine and improved population health management.