Cardiovascular Imaging using Machine Learning: A Review

Abstract

Cardiovascular diseases are a major cause of death worldwide, making early detection and diagnosis critical for reducing mortality and morbidity. The interpretation of complex medical images can be made easier with the use of machine learning algorithms, which could result in more precise cardiovascular imaging diagnosis. In this review paper, we give an overview of the state-of-the-art in machine learning-based cardiovascular imaging, including the datasets, imaging modalities, and algorithms that are currently accessible. We also discuss the major challenges and opportunities in the field and highlight recent advances in machine learning algorithms for automated cardiac image analysis. Specifically, we focus on the use of deep learning and convolutional neural networks for cardiac image segmentation and classification of cardiac conditions, such as heart failure, myocardial infarction, and arrhythmias. We explore the potential of these algorithms to improve the accuracy and efficiency of cardiovascular imaging and discuss the need for standardized datasets and evaluation metrics to enable better comparison of different algorithms. We also discuss the importance of interpretability in machine learning algorithms to enhance trust and transparency in their predictions. Overall, this review provides a comprehensive overview of the current state and future potential of machine learning in cardiovascular imaging, highlighting its significant impact on improving the diagnosis and treatment of cardiovascular diseases.