| Literature DB >> 31737539 |
Arghavan Arafati1, Peng Hu2, J Paul Finn2, Carsten Rickers3, Andrew L Cheng4,5, Hamid Jafarkhani6, Arash Kheradvar1.
Abstract
Cardiac MRI (CMR) allows non-invasive, non-ionizing assessment of cardiac function and anatomy in patients with congenital heart disease (CHD). The utility of CMR as a non-invasive imaging tool for evaluation of CHD have been growing exponentially over the past decade. The algorithms based on artificial intelligence (AI), and in particular, deep learning, have rapidly become a methodology of choice for analyzing CMR. A wide range of applications for AI have been developed to tackle challenges in various aspects of CMR, and significant advances have also been made from image acquisition to image analysis and diagnosis. We include an overview of AI definitions, different architectures, and details on well-known methods. This paper reviews the major deep learning concepts used for analyses of patients with CHD. In the end, we have summarized a list of open challenges and concerns to be considered for future studies. 2019 Cardiovascular Diagnosis and Therapy. All rights reserved.Entities:
Keywords: Cardiac MRI (CMR); artificial intelligence (AI); cardiac segmentation; congenital heart disease (CHD); deep learning
Year: 2019 PMID: 31737539 PMCID: PMC6837938 DOI: 10.21037/cdt.2019.06.09
Source DB: PubMed Journal: Cardiovasc Diagn Ther ISSN: 2223-3652