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01.11.2020 | Cardiac Computed Tomography (A. Crean and G. Small, Section Editors) | Ausgabe 11/2020

Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis

Zeitschrift:: Current Cardiovascular Imaging Reports > Ausgabe 11/2020

Autoren:: Heon Lee, Tilman Emrich, U. Joseph Schoepf, Verena Brandt, Tyler J. Leonard, Hunter N. Gray, Vincent M. Giovagnoli, Danielle M. Dargis, Jeremy R. Burt, Christian Tesche

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Wichtige Hinweise

This article is part of the Topical Collection on Cardiac Computed Tomography

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Abstract

Purpose of Review

To review the current applications of artificial intelligence (AI) for coronary artery calcium scoring (CACS) and plaque analysis with their achievements and potential clinical impacts.

Recent Findings

Recent advances of AI-based technologies especially deep learning (DL) approaches in medical imaging have achieved substantial progress in automated detection and characterization of coronary atherosclerotic plaques, providing promising results for AI application in diagnosis and management of coronary artery disease.

Summary

To date, many studies investigated the potential role of DL in CACS and showed promising results for clinical application in a variety of CT examinations, demonstrating excellent agreement compared with manual scoring. DL-based approaches have also provided considerable progress in automated analysis of non-calcified plaque. Many investigations have shown that automated localization and classification of non-calcified plaque and luminal stenosis is feasible in cardiac CT angiography, although still challenging.

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Titel: Artificial Intelligence in Cardiac CT: Automated Calcium Scoring and Plaque Analysis
Autoren:: Heon Lee
Tilman Emrich
U. Joseph Schoepf
Verena Brandt
Tyler J. Leonard
Hunter N. Gray
Vincent M. Giovagnoli
Danielle M. Dargis
Jeremy R. Burt
Christian Tesche
Publikationsdatum: 01.11.2020
DOI: https://doi.org/10.1007/s12410-020-09549-9
Verlag: Springer US
Zeitschrift: Current Cardiovascular Imaging Reports
Ausgabe 11/2020
Print ISSN: 1941-9066
Elektronische ISSN: 1941-9074

Sie können e.Med Innere Medizin 14 Tage kostenlos testen (keine Print-Zeitschrift enthalten). Der Test läuft automatisch und formlos aus. Es kann nur einmal getestet werden.
Sie können e.Med Radiologie 14 Tage kostenlos testen (keine Print-Zeitschrift enthalten). Der Test läuft automatisch und formlos aus. Es kann nur einmal getestet werden.