Clinical Inference From Cardiovascular Imaging: Paradigm Shift Towards Machine-Based Intelligent Platform

Diagnostic imaging plays an indispensable role in the modern management of cardiovascular diseases. Despite significant advances in imaging modalities, the workflow in clinical imaging requires a great deal of human input. With increasing demands in patient care, current workflows in cardiovascular imaging face a number of challenges which include insufficient number of specialists, time constraints, learning curves, and inter-observer variability. Automated techniques using revolutionary approaches with machine learning can fundamentally alter our interpretation process and produce smarter, faster, and efficient results. With the emergence of big data and deep learning techniques which enable direct inference of image data without intensive feature engineering or can process raw images directly. Machine learning has been rapidly adopted in the field of medicine and has shown its fast and reproducible automatic inference in various cardiovascular imaging modalities, including echocardiography, nuclear imaging, cardiac computed tomography, and cardiac magnetic resonance. While deep domain knowledge of cardiovascular disease remains the cornerstone throughout the process, physicians may be relieved from mundane tasks using automation and be able to spend more time on decision-making and communication with patients and other medical staff in the new paradigm brought by machine learning. In this review article, we explore the role of machine in the clinical inference from cardiac imaging data recently.