Artificial intelligence in coronary computed tomography angiography

doi:10.35711/aimi.v2.i3.73

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Jun 28, 2021 (publication date) through Nov 4, 2024

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Artificial Intelligence in Medical Imaging

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2644-3260

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Artif Intell Med Imaging. Jun 28, 2021; 2(3): 73-85
Published online Jun 28, 2021. doi: 10.35711/aimi.v2.i3.73

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Figure 2 The workflow of the fractional flow reserve-computed tomography derivation. ¹A total of 12000 coronary anatomies were generated; ²twenty-eight geometric features were extracted from the synthetically generated database; ³a deep neural network with four hidden layers was used to train the machine learning-based model. FFR-CT: Fractional flow reserve-computed tomography; CCTA: Coronary computed tomography angiography.

Citation: Zhang ZZ, Guo Y, Hou Y. Artificial intelligence in coronary computed tomography angiography. Artif Intell Med Imaging 2021; 2(3): 73-85
URL: https://www.wjgnet.com/2644-3260/full/v2/i3/73.htm
DOI: https://dx.doi.org/10.35711/aimi.v2.i3.73