Inter-Operator Reliability of an Onsite Machine Learning-Based Prototype to Estimate Ct Angiography-Derived Fractional Flow Reserve

Abstract

Background: Advances in computed tomography (CT) and machine learning have enabled on-site non-invasive assessment of fractional ow reserve (FFR CT ).Purpose: To assess the inter-operator variability of Coronary CT Angiography-derived FFR CT using a machine learning based post-processing prototype.Materials and Methods: We included 60 symptomatic patients who underwent coronary CT angiography. FFR CT was calculated by 2 independent operators after training using a machine learning based on-site prototype. FFR CT was measured 1 cm distal to the coronary plaque or in the middle of the segments if no coronary lesions were present. Intraclass correlation coe cient (ICC) and Bland-Altman analysis were used to evaluate inter-operator variability effect in FFR CT estimates. Sensitivity analysis was done by cardiac risk factors, degree of stenosis and image quality.Results: A total of 535 coronary segments in 60 patients were assessed. The overall ICC was 0.986 per patient (95% CI: 0.977 -0.992) and 0.972 per segment (95% CI: 0.967 -0.977). The absolute mean difference in FFR CT estimates was 0.012 per patient (95% CI for limits of agreement: -0.035 -0.039) and 0.02 per segment (95% CI for limits of agreement: -0.077 -0.080). Tight limits of agreement were seen on Bland-Altman analysis. Distal segments had greater variability compared to proximal/mid segments (absolute mean difference 0.011 vs 0.025, p<0.001). Results were similar on sensitivity analysis. Conclusion:A high degree of inter-operator reproducibility can be achieved by onsite machine learning based FFR CT assessment. Future research is required to evaluate the physiological relevance and prognostic value of FFR CT .