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Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

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

Artificial Intelligence to Improve Risk Prediction with Nuclear Cardiac Studies

Ruijsink Bram
Benjamins Jan-Walter
Knuuti Juhani
Juarez-Orozco Luis Eduardo
Daquarti Gustavo
Klén Riku
van Es Rene
Niemi Mikael
van der Harst Pim
Yeung Ming Wai
Katso/Avaa
Juarez-OrozcoEtAl2022ArtificialIntelligenceToImprove.pdf (924.6Kb)
Lataukset: 

Springer
doi:10.1007/s11886-022-01649-w
URI
https://link.springer.com/article/10.1007/s11886-022-01649-w
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022081154222
Tiivistelmä

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.

Recent Findings and Summary
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.

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