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Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology

Authors :
Robert J H, Miller
Cathleen, Huang
Joanna X, Liang
Piotr J, Slomka
Source :
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology. 29(4)
Publication Year :
2022

Abstract

Artificial intelligence (AI) techniques have emerged as a highly efficient approach to accurately and rapidly interpret diagnostic imaging and may play a vital role in nuclear cardiology. In nuclear cardiology, there are many clinical, stress, and imaging variables potentially available, which need to be optimally integrated to predict the presence of obstructive coronary artery disease (CAD) or predict the risk of cardiovascular events. In spite of clinical awareness of a large number of potential variables, it is difficult for physicians to integrate multiple features consistently and objectively. Machine learning (ML) is particularly well suited to integrating this vast array of information to provide patient-specific predictions. Deep learning (DL), a branch of ML characterized by a multi-layered convolutional model architecture, can extract information directly from images and identify latent image features associated with a specific prediction. This review will discuss the latest AI applications to disease diagnosis and risk prediction in nuclear cardiology with a focus on potential clinical applications.

Details

ISSN :
15326551
Volume :
29
Issue :
4
Database :
OpenAIRE
Journal :
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Accession number :
edsair.pmid..........b39cb855ea028b123abacdeac0463908