Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2021 Jun;11(6):2208-2213.
doi: 10.21037/qims-21-99.

Machine learning-based advances in coronary computed tomography angiography

Mina M Benjamin  1 , Mark G Rabbat  1
Affiliations
Editorial

Machine learning-based advances in coronary computed tomography angiography

Mina M Benjamin et al. Quant Imaging Med Surg. 2021 Jun.
No abstract available

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-21-99). Dr. MGR: Consultant: HeartFlow. Dr. MMB has no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Examples of sureplaqueTM software analysis of plaque composition and vessel volume.
Figure 2
Figure 2
Example of FFRCT report for a patient with CAD. FFRCT, coronary computed tomography angiography-derived fractional flow reserve; CAD, coronary artery disease; LCX, left circumflex artery; RCA, right coronary artery.
See this image and copyright information in PMC

References

    1. Muscogiuri G, Van Assen M, Tesche C, De Cecco CN, Chiesa M, Scafuri S, Guglielmo M, Baggiano A, Fusini L, Guaricci AI, Rabbat MG, Pontone G. Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis. Biomed Res Int 2020;2020:6649410. 10.1155/2020/6649410 - DOI - PMC - PubMed
    1. Ahmadi A, Argulian E, Leipsic J, Newby DE, Narula J. From Subclinical Atherosclerosis to Plaque Progression and Acute Coronary Events: JACC State-of-the-Art Review. J Am Coll Cardiol 2019;74:1608-17. 10.1016/j.jacc.2019.08.012 - DOI - PubMed
    1. Muscogiuri G, Chiesa M, Trotta M, Gatti M, Palmisano V, Dell'Aversana S, Baessato F, Cavaliere A, Cicala G, Loffreno A, Rizzon G, Guglielmo M, Baggiano A, Fusini L, Saba L, Andreini D, Pepi M, Rabbat MG, Guaricci AI, De Cecco CN, Colombo G, Pontone G. Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA. Atherosclerosis 2020;294:25-32. 10.1016/j.atherosclerosis.2019.12.001 - DOI - PubMed
    1. Akutagawa O, Kijima Y, Kume K, Sakai T, Okura A, Ide K, Iwasaki S, Hata T. Feasibility and limitation of coronary plaque volumetry by contrast-enhanced 64-row multi-detector computed tomography. Int J Cardiol 2011;150:118-20. 10.1016/j.ijcard.2011.04.033 - DOI - PubMed
    1. Brodoefel H, Burgstahler C, Heuschmid M, Reimann A, Khosa F, Kopp A, Schroeder S, Claussen CD, Clouse ME. Accuracy of dual-source CT in the characterisation of non-calcified plaque: use of a colour-coded analysis compared with virtual histology intravascular ultrasound. Br J Radiol 2009;82:805-12. 10.1259/bjr/35768497 - DOI - PMC - PubMed

Publication types