Abstract
Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated. Both OCT and FFR data were obtained for left anterior descending artery lesions in 125 patients. Training and testing groups were partitioned in the ratio of 5:1. The OCT-based machine learning-FFR was derived for the testing group and compared with wire-based FFR in terms of ischemia diagnosis (FFR ≤ 0.8). The OCT-based machine learning-FFR showed good correlation (r = 0.853, P < 0.001) with the wire-based FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the OCT-based machine learning-FFR for the testing group were 100%, 92.9%, 87.5%, 100%, and 95.2%, respectively. The OCT-based machine learning-FFR can be used to simultaneously acquire information on both image and functional modalities using one procedure, suggesting that it may provide optimized treatments for intermediate coronary artery stenosis.
Highlights
Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated
When considering percutaneous coronary intervention (PCI) for ischemia based on FFR, the lack of anatomical information on atherosclerotic plaques can be problematic in patients, especially those with acute coronary syndrome[1]
Previous studies reported that the simulations of OCT-derived computational flow dynamics (CFD) allowed additional functional estimates of FFR, demonstrating a good correlation with invasive FFR m easurements[2,3,4,5]
Summary
Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated. Both OCT and FFR data were obtained for left anterior descending artery lesions in 125 patients. The combination of OCT and FFR measurements may provide additional information to guide the application of an appropriate treatment strategy. Using both strategies in all clinical practices increases time and cost. Previous studies reported that the simulations of OCT-derived computational flow dynamics (CFD) allowed additional functional estimates of FFR, demonstrating a good correlation with invasive FFR m easurements[2,3,4,5].
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