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- W3173641004 abstract "This editorial refers to ‘Impact of coronary calcium score and lesion characteristics on the diagnostic performance of machine learning-based computed tomography-derived fractional flow reserve’ by H.J. Koo et al., 22:998--1006. Scientific progress is driven by a continuous need to bring the status of knowledge and its implementation forward. This process commonly shows a pendulum phenomenon in which early positive results capture the interest and imagination of clinicians and researchers. Thereon, the evidence tends to balance out bringing about nuances and an outlook of the possibilities and limitations that ultimately ground the novelty in its suitable niche. This evolution of novel implementations additionally reminds us of the value of existing methods and their unframed utility. This has been the case in the permeation of machine learning analytics into cardiovascular imaging, where current non-invasive techniques such as computed tomography (CT) currently deliver a myriad of information regarding coronary anatomy that can..." @default.
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- W3173641004 date "2021-06-24" @default.
- W3173641004 modified "2023-10-06" @default.
- W3173641004 title "Machine learning in defining computed tomography-derived fractional flow reserve" @default.
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- W3173641004 doi "https://doi.org/10.1093/ehjci/jeab124" @default.
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