Abstract
The tremendous advances in digital information and communication technology have entered everything from our daily lives to the most intricate aspects of medical and surgical care. These advances are seen in electronic and mobile health and allow many new applications to further improve and make the diagnoses of patient diseases and conditions more precise. In the area of digital radiology with respect to diagnostics, the use of advanced imaging tools and techniques is now at the center of evaluation and treatment. Digital acquisition and analysis are central to diagnostic capabilities, especially in the field of cardiovascular imaging. Furthermore, the introduction of artificial intelligence (AI) into the world of digital cardiovascular imaging greatly broadens the capabilities of the field both with respect to advancement as well as with respect to complete and accurate diagnosis of cardiovascular conditions. The application of AI in recognition, diagnostics, protocol automation, and quality control for the analysis of cardiovascular imaging modalities such as echocardiography, nuclear cardiac imaging, cardiovascular computed tomography, cardiovascular magnetic resonance imaging, and other imaging, is a major advance that is improving rapidly and continuously. We document the innovations in the field of cardiovascular imaging that have been brought about by the acceptance and implementation of AI in relation to healthcare professionals and patients in the cardiovascular field.
Highlights
Received: 19 November 2021In this age of technology, there have been numerous inventions created to expand the boundaries of medical treatment and diagnosis beyond their current capabilities
Arsajani et al found that the accuracy of predicting Coronary artery disease (CAD) with an myocardial perfusion imaging (MPI) device improved significantly when in adjunct with a learning algorithm [22]
Al’Aref et al.’s results showed a significantly more accurate assessment of obstructive CAD from computed tomography (CT) imaging using machine learning with the coronary artery calcium score [21]
Summary
In this age of technology, there have been numerous inventions created to expand the boundaries of medical treatment and diagnosis beyond their current capabilities. As it works from a refer to when models are trained to analyze algorithms based on reference data that have already dataset and applies the same pattern to a new dataset, supervised learning is been entered. Lear machine learning has its limitations, especially apparent when applied to the fieldmachine of can lead to bias when it comes to analyzing the dataset. Deep learning is most valuable with pattern recognition and image identification, when working with large image datasets It is most effective for cardiovascular imaging, such as echocardiography, angiography, and cardiac magnetic resonance. Simpler machine learning would be easier to use for datasets that are more defined and structured
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