-3bf97310184a13a8c1e-1c1 20230225013558687 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 03 30 2023 11 6 Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University, for Women Delhi, India Rachana Pandey https://orcid.org/0000-0002-9404-2548 Monika Choudhary https://orcid.org/0000-0003-1579-140X Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University, for Women Delhi, India 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. 03 30 2023 39 49 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true No, we did not receive. No conflicts of interest to the best of our knowledge. No, the article does not require ethical approval and consent to participate with evidence. Not relevant. Ms. Rachana Pandey wrote the initial manuscript draft and contributed to study conception and design. She performed the literature search and categorization, developed the ML pipeline model, and wrote the imaging-type literature section. Ms. Pandey also coordinated the remaining sections, and wrote the dataset, application, and algorithm sections, as well as the abstract, introduction, limitation, future perspective, and conclusion sections. Ms. Monika Choudhary supervised the research and provided critical feedback on the manuscript. She provided valuable guidance and support throughout the research process for this paper. She offered valuable insights on the topic and helped to refine the research methodology. Her feedback and suggestions greatly improved the quality of the paper. Both authors contributed to data analysis and interpretation, and approved the final manuscript for submission. 10.35940/ijrte.F7480.0311623 https://www.ijrte.org/portfolio-item/F74800311623/