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Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease

Crossref DOI link: https://doi.org/10.1007/s00330-020-06964-w

Published Online: 2020-05-28

Published Print: 2020-11

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Qiao, Hong Yan

Tang, Chun Xiang

Schoepf, U. Joseph

Tesche, Christian

Bayer, Richard R. 2nd

Giovagnoli, Dante A

Todd Hudson, H. Jr

Zhou, Chang Sheng

Yan, Jing

Lu, Meng Jie

Zhou, Fan

Lu, Guang Ming

Jiang, Jian Wei

Zhang, Long Jiang https://orcid.org/0000-0002-6664-7224
Funding

Funding for this research was provided by:

The National Key Research and Development Program of China (2017YFC0113400)
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Text and Data Mining valid from 2020-05-28

Version of Record valid from 2020-05-28
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Article History

Received: 3 December 2019

Revised: 2 April 2020

Accepted: 15 May 2020

First Online: 28 May 2020

Compliance with ethical standards

:

: The scientific guarantor of this publication is Long Jiang Zhang.

: U. Joseph Schoepf is a consultant for and/or receives research support from Astellas, Bayer, Bracco, Elucid BioImaging, General Electric, Guerbet, HeartFlow, and Siemens Healthineers. The other authors have no conflicts of interest to disclose.

: Meng Jie Lu kindly provided statistical advice for this manuscript. No complex statistical methods were necessary for this paper.

: Written informed consent was waived by the Institutional Review Board.

: Institutional Review Board approval was obtained.

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