Interoperator reliability of an on-site machine learning-based prototype to estimate CT angiography-derived fractional flow reserve

Abstract

BackgroundAdvances in CT and machine learning have enabled on-site non-invasive assessment of fractional flow reserve (FFRCT).PurposeTo assess the interoperator and intraoperator variability of coronary CT angiography-derived FFRCT using a machine learning-based postprocessing prototype.Materials and methodsWe included 60 symptomatic patients who underwent coronary CT angiography. FFRCT was calculated by two independent operators after training using a machine learning-based on-site prototype. FFRCT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coefficient (ICC) and Bland-Altman analysis were used to evaluate interoperator variability effect in FFRCT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.ResultsA total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI 0.977 to 0.992) and 0.972 per segment (95% CI 0.967 to 0.977). The absolute mean difference in FFRCT estimates was 0.012 per patient (95% CI for limits of agreement: −0.035 to 0.039) and 0.02 per segment (95% CI for limits of agreement: −0.077 to 0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared with proximal/mid segments (absolute mean difference 0.011 vs 0.025, p<0.001). Results were similar on sensitivity analysis.ConclusionA high degree of interoperator and intraoperator reproducibility can be achieved by on-site machine learning-based FFRCT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFRCT.