A machine-learning approach for computation of fractional flow reserve from coronary computed tomography

• Published in: Journal of applied physiology (1985) Vol. 121; no. 1; pp. 42 - 52
• Main Authors: Itu, Lucian, Rapaka, Saikiran, Passerini, Tiziano, Georgescu, Bogdan, Schwemmer, Chris, Schoebinger, Max, Flohr, Thomas, Sharma, Puneet, Comaniciu, Dorin
• Format: Journal Article
• Language: English
• Published: United States American Physiological Society 01.07.2016
• Subjects: Artificial intelligence; Blood pressure; Catheters; Coronary Angiography - methods; Coronary Stenosis - physiopathology; Coronary vessels; Coronary Vessels - physiopathology; Fractional Flow Reserve, Myocardial - physiology; Heart - physiopathology; Machine Learning; Models, Biological; Physics; Sensitivity and Specificity; Tomography; Tomography, X-Ray Computed - methods; coronary artery disease; CCTA; FFR; machine learning; synthetic database
• ISSN: 8750-7587; 1522-1601
• DOI: 10.1152/japplphysiol.00752.2015

Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor.