TY - JOUR
T1 - Artificial intelligence and cardiovascular imaging
T2 - A win-win combination
AU - Badano, Luigi P.
AU - Keller, Daria M.
AU - Muraru, Denisa
AU - Torlasco, Camilla
AU - Parati, Gianfranco
N1 - Publisher Copyright:
© 2020 Turkish Society of Cardiology. All rights reserved.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.
AB - Rapid development of artificial intelligence (AI) is gaining grounds in medicine. Its huge impact and inevitable necessity are also reflected in cardiovascular imaging. Although AI would probably never replace doctors, it can significantly support and improve their productivity and diagnostic performance. Many algorithms have already proven useful at all stages of the cardiac imaging chain. Their crucial practical applications include classification, automatic quantification, notification, diagnosis, and risk prediction. Consequently, more reproducible and repeatable studies are obtained, and personalized reports may be available to any patient. Utilization of AI also increases patient safety and decreases healthcare costs. Furthermore, AI is particularly useful for beginners in the field of cardiac imaging as it provides anatomic guidance and interpretation of complex imaging results. In contrast, lack of interpretability and explainability in AI carries a risk of harmful recommendations. This review was aimed at summarizing AI principles, essential execution requirements, and challenges as well as its recent applications in cardiovascular imaging.
KW - Artificial intelligence
KW - Cardiac computed tomography
KW - Cardiac magnetic resonance
KW - Deep learning
KW - Echocardiography
KW - Machine learning
KW - Nuclear cardiac imaging
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U2 - 10.14744/AnatolJCardiol.2020.94491
DO - 10.14744/AnatolJCardiol.2020.94491
M3 - Review article
C2 - 33001058
AN - SCOPUS:85092501909
VL - 24
SP - 214
EP - 223
JO - Anatolian journal of cardiology
JF - Anatolian journal of cardiology
SN - 2149-2263
IS - 4
ER -