Exploring the Frontiers of Unsupervised Learning Techniques for Diagnosis of Cardiovascular Disorder: A Systematic Review

• Published in: IEEE access Vol. 12; pp. 139253 - 139272
• Main Authors: Priyadarshi, Rahul, Ranjan, Rakesh, Kumar Vishwakarma, Anish, Yang, Tiansheng, Singh Rathore, Rajkumar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Artificial intelligence; Biomedical imaging; cardiac imaging; Cardiovascular diseases; cardiovascular disorder; Clustering; Clustering algorithms; Data augmentation; Datasets; Diagnosis; Diseases; Generative adversarial networks; generative models; Heart; Literature reviews; Machine learning; Medical imaging; R&D; Research & development; Unsupervised learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3468163

Accurate diagnosis and treatment of cardiovascular diseases require the integration of cardiac imaging, which provides crucial information about the structure and function of the heart to improve overall patient care. This review explores the role of Artificial Intelligence (AI) in advancing cardiac imaging analysis, with a focus on unsupervised learning methods. Unlike supervised AI systems, which rely on annotated datasets, the use of unsupervised learning proves to be a game-changer. It effectively tackles issues related to limited datasets and sets the stage for scalable and adaptive solutions in cardiac imaging. This paper gives a comprehensive overview of the limitations of traditional methods and the potential of unsupervised AI in overcoming challenges related to dataset scarcity through an extensive literature review and analysis of unsupervised algorithms, including clustering techniques, dimensionality reduction, and generative models. This review study highlights the contributions of unsupervised techniques for enhancing diagnostic accuracy and efficiency in cardiac imaging. By comparing unsupervised and supervised methods, the paper aims to explain the benefits and limitations of each approach, offering valuable insights for advancing AI integration in cardiac healthcare. The findings are expected to guide future research and development, leading to innovative advancements in cardiovascular diagnostics.