Skip to main content
Top
Published in: La radiologia medica 11/2020

01-11-2020 | Artificial Intelligence | Cardiac Radiology

Artificial intelligence in cardiac radiology

Authors: Marly van Assen, Giuseppe Muscogiuri, Damiano Caruso, Scott J. Lee, Andrea Laghi, Carlo N. De Cecco

Published in: La radiologia medica | Issue 11/2020

Login to get access

Abstract

Artificial intelligence (AI) is entering the clinical arena, and in the early stage, its implementation will be focused on the automatization tasks, improving diagnostic accuracy and reducing reading time. Many studies investigate the potential role of AI to support cardiac radiologist in their day-to-day tasks, assisting in segmentation, quantification, and reporting tasks. In addition, AI algorithms can be also utilized to optimize image reconstruction and image quality. Since these algorithms will play an important role in the field of cardiac radiology, it is increasingly important for radiologists to be familiar with the potential applications of AI. The main focus of this article is to provide an overview of cardiac-related AI applications for CT and MRI studies, as well as non-imaging-based applications for reporting and image optimization.
Literature
2.
go back to reference McCarthy J (1990) Artificial intelligence, logic and formalizing common sense. Philos Log Artif Intell 1990:161–190 McCarthy J (1990) Artificial intelligence, logic and formalizing common sense. Philos Log Artif Intell 1990:161–190
5.
go back to reference Mukherjee S (2017) A.I. vs M.D. What happens when diagnosis is automated. New Yorker, New York Mukherjee S (2017) A.I. vs M.D. What happens when diagnosis is automated. New Yorker, New York
6.
go back to reference Frost & Sullivan (2015) Cognitive computing and artificial intelligence systems in healthcare. Ramping up a $6 billion dollar market opportunity. Frost & Sullivan, New York Frost & Sullivan (2015) Cognitive computing and artificial intelligence systems in healthcare. Ramping up a $6 billion dollar market opportunity. Frost & Sullivan, New York
14.
go back to reference Arnett DK, Blumenthal RS, Albert MA et al (2019) 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 74(10):1376–1414. https://doi.org/10.1016/j.jacc.2019.03.009CrossRef Arnett DK, Blumenthal RS, Albert MA et al (2019) 2019 ACC/AHA guideline on the primary prevention of cardiovascular disease: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 74(10):1376–1414. https://​doi.​org/​10.​1016/​j.​jacc.​2019.​03.​009CrossRef
23.
go back to reference Tao Q, Lelieveldt BPF, Van Der Geest RJ (2020) Deep learning for quantitative cardiac MRI. Am J Roentgenol 214:529–535CrossRef Tao Q, Lelieveldt BPF, Van Der Geest RJ (2020) Deep learning for quantitative cardiac MRI. Am J Roentgenol 214:529–535CrossRef
27.
go back to reference Foldyna B, Udelson JE, Karády J et al (2019) Pretest probability for patients with suspected obstructive coronary artery disease: re-evaluating Diamond-Forrester for the contemporary era and clinical implications: insights from the PROMISE trial. Eur Heart J Cardiovasc Imaging. https://doi.org/10.1093/ehjci/jey182CrossRef Foldyna B, Udelson JE, Karády J et al (2019) Pretest probability for patients with suspected obstructive coronary artery disease: re-evaluating Diamond-Forrester for the contemporary era and clinical implications: insights from the PROMISE trial. Eur Heart J Cardiovasc Imaging. https://​doi.​org/​10.​1093/​ehjci/​jey182CrossRef
33.
go back to reference Greenland P, Bonow RO, Brundage BH et al (2007) ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain. A report of the American College of Cardiology Foundation Cl. J Am Coll Cardiol 49(3):378–402. https://doi.org/10.1016/j.jacc.2006.10.001CrossRef Greenland P, Bonow RO, Brundage BH et al (2007) ACCF/AHA 2007 clinical expert consensus document on coronary artery calcium scoring by computed tomography in global cardiovascular risk assessment and in evaluation of patients with chest pain. A report of the American College of Cardiology Foundation Cl. J Am Coll Cardiol 49(3):378–402. https://​doi.​org/​10.​1016/​j.​jacc.​2006.​10.​001CrossRef
57.
go back to reference Van Hamersvelt RW (2018) Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol 29(5):2350–2359CrossRef Van Hamersvelt RW (2018) Deep learning analysis of left ventricular myocardium in CT angiographic intermediate-degree coronary stenosis improves the diagnostic accuracy for identification of functionally significant stenosis. Eur Radiol 29(5):2350–2359CrossRef
74.
go back to reference Moss MD, Bigger JT, Case R, Gillespie MD, Goldstein RE (2015) Risk stratification and survival after myocardial infarction. N Engl J Med 309(6):331–336 Moss MD, Bigger JT, Case R, Gillespie MD, Goldstein RE (2015) Risk stratification and survival after myocardial infarction. N Engl J Med 309(6):331–336
78.
89.
go back to reference Isensee F, Jaeger PF, Full PM, Wolf I, Engelhardt S, Maier-Hein KH (2018) Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). https://doi.org/10.1007/978-3-319-75541-0_13 Isensee F, Jaeger PF, Full PM, Wolf I, Engelhardt S, Maier-Hein KH (2018) Automatic cardiac disease assessment on cine-MRI via time-series segmentation and domain specific features. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). https://​doi.​org/​10.​1007/​978-3-319-75541-0_​13
90.
107.
go back to reference Florian A, Jurcut R, Ginghina C, Bogeart J (1997) Cardiac magnetic resonance imaging in ischemic heart disease a clinical review. J Med Life 4(4):330–345 Florian A, Jurcut R, Ginghina C, Bogeart J (1997) Cardiac magnetic resonance imaging in ischemic heart disease a clinical review. J Med Life 4(4):330–345
114.
115.
go back to reference Fahmy AS, Rausch J, Neisiusa U et al (2018) Fully automated quantification of cardiac MR LV mass and scar in hypertrophic cardiomyopathy using deep learning. Circulation 138(Suppl_1):A15085 Fahmy AS, Rausch J, Neisiusa U et al (2018) Fully automated quantification of cardiac MR LV mass and scar in hypertrophic cardiomyopathy using deep learning. Circulation 138(Suppl_1):A15085
119.
130.
go back to reference European-Commission (2020) White paper on artificial intelligence European-Commission (2020) White paper on artificial intelligence
Metadata
Title
Artificial intelligence in cardiac radiology
Authors
Marly van Assen
Giuseppe Muscogiuri
Damiano Caruso
Scott J. Lee
Andrea Laghi
Carlo N. De Cecco
Publication date
01-11-2020
Publisher
Springer Milan
Published in
La radiologia medica / Issue 11/2020
Print ISSN: 0033-8362
Electronic ISSN: 1826-6983
DOI
https://doi.org/10.1007/s11547-020-01277-w

Other articles of this Issue 11/2020

La radiologia medica 11/2020 Go to the issue