Integration of Machine Learning in Imaging Analysis for Clinical Diagnosis of Cardiovascular Diseases

In recent years, artificial intelligence (AI) has emerged as an important technology revolutionizing the diagnosis and prognosis of cardiovascular diseases. In cardiology, machine learning models can reduce the cost and time-to-diagnosis, as well as increase diagnostic precision, consequently improving the quality of care for patients. Therefore, this review provided an overview of innovative models in cardiology-based image analysis for disease diagnosis. Recent reports on studies evaluating the performance of these models were reviewed. Common approaches to training AI models include supervised learning, semi-supervised learning, unsupervised learning, and various other approaches. Logistic regression, support vector machines, random forests, cluster analysis, and neural networks are examples of machine learning architectures used in modern times. AI is used in cardiac magnetic resonance imaging to detect artifacts and assess image quality, as well as to quantify blood flow faster and more accurately. Convoluted neural networks are utilized in echocardiography and electrocardiography to enhance the precision of visualizing cardiac structures and assessing cardiac functions, respectively. AI models in X-ray imaging are employed to identify structural anomalies in the heart and assess their severity. In response to the need for high-quality data while safeguarding patient privacy and confidentiality, specialized databases with comprehensive datasets have been established, such as the UK Biobank and the MIT-BIH Malignant Ventricular Arrhythmia Database. Prospects in machine learning-based screening in cardiology include digital heart winners in precision medicine, explainable AI, and radionics.