Current Challenges and Recent Updates in Artificial Intelligence and Echocardiography

AbstractPurpose of the ReviewThis review discusses the recent advances in automated echocardiography using artificial intelligence and machine learning (ML) techniques. Specific emphasis is placed on the potential for machine learning-based methods to improve accuracy and reproducibility of echocardiographic assessment as well as early cardiovascular disease detection and personalized risk assessment.Recent FindingsEchocardiography remains the first line imaging modality for evaluation of many cardiovascular diseases. The last few years have witnessed a rapid expansion and growth of ML-based automated analysis and interpretation of echocardiography. These ML algorithms have shown great promise for improving data reliability, accuracy, and reproducibility of echocardiographic results. We anticipate that the application of ML algorithms will further expand the indications of echocardiography to include diseases that are traditionally only diagnosed with the more advanced imaging modalities such as cardiac magnetic resonance imaging. The ability to leverage ML ’s robust capability for processing large and complex datasets will result in improved diagnosis of cardiovascular disease at subclinical stages, enable prediction of disease progression and prognosis, and facilitate the characterization of disease phenotypes to allow more targeted therapies.SummaryThe paradigm is rapidly shifting in the field of echocardiography with the emergence of ML algorithms that are promising to ...
Source: Current Cardiovascular Imaging Reports - Category: Radiology Source Type: research