A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.
By Lucian Mihai Itu, Saikiran Rapaka, Tiziano Passerini, Bogdan Georgescu, Chris Schwemmer, Max Schoebinger, Thomas G. Flohr, Puneet S. Sharma, Dorin Comaniciu. Journal of applied physiology, 2016
380 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging, Surgery
View Paper: https://www.semanticscholar.org/paper/d2f7a6054c9f85e6cd2e002085e48b6dafe4d0ee
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Citations in the corpus
- Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “91 for both), with the DL algorithm requiring significantly shorter execution times (about 2 s) (63,85).”
- Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. (2018, methodology): “55 58 In related work, ML (LogitBoost algorithm) has been applied by Dey et al. 20 in a multicentre study of 254 patients with CCTA to predict the probability of low value by invasive FFR by considering a number of CCTA image features derived by vessel extraction and plaque characterization…”