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. Author manuscript; available in PMC: 2020 Mar 26.
Published in final edited form as: J Am Coll Cardiol. 2019 Mar 26;73(11):1317–1335. doi: 10.1016/j.jacc.2018.12.054

FIGURE 6. Information Gain for Age, Sex, and Quantitative CTA Measures for Lesion-Specific Ischemia.

FIGURE 6

In the left panel, measures directly related to plaque volumes are in light blue and the remaining measures are in dark blue. Variables with information gain >0.001 were used in machine learning. Contrast density difference had the highest information gain among quantitative CTA metrics. Reproduced with permission from Dey et al. (75). The right panel shows an example of the machine learning prediction of lesion-specific ischemia in a patient undergoing CTA. NCP and CP are shown in red and yellow image overlay in the left anterior descending (LAD) artery of a 67-year-old male symptomatic patient undergoing CTA, along with the integrated machine learning ischemia risk score (60% in the LAD). Invasive FFR measured in the LAD artery was 0.73. FFR = fractional flow reserve; LCX = left circumflex artery; RCA = right coronary artery; other abbreviations as in Figures 4 and 5.