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Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

Research output: Contribution to journalReview articlepeer-review

Luis Eduardo Juarez-Orozco, Riku Klén, Mikael Niemi, Bram Ruijsink, Gustavo Daquarti, Rene van Es, Jan Walter Benjamins, Ming Wai Yeung, Pim van der Harst, Juhani Knuuti

Original languageEnglish
Pages (from-to)307-316
Number of pages10
JournalCurrent cardiology reports
Volume24
Issue number4
DOIs
Published1 Apr 2022

Bibliographical note

Publisher Copyright: © 2022. The Author(s).

King's Authors

Abstract

PURPOSE OF REVIEW: As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines.

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