Diagnostic performance of deep learning and computational fluid dynamics-based instantaneous wave-free ratio derived from computed tomography angiography.

BACKGROUND AND OBJECTIVES: Both fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR) are widely used to evaluate ischemia-causing coronary lesions. A new method of CT-iFR, namely AccuiFRct, for calculating iFR based on deep learning and computational fluid dynamics (CFD) using coronary computed tomography angiography (CCTA) has been proposed. In this study, the diagnostic performance of AccuiFRct was thoroughly assessed using iFR as the reference standard. METHODS: Data of a total of 36 consecutive patients with 36 vessels from a single-center who underwent CCTA, invasive FFR, and iFR were retrospectively analyzed. The CT-derived iFR values were computed using a novel deep learning and CFD-based model. RESULTS: Mean values of FFR and iFR were 0.80 ± 0.10 and 0.91 ± 0.06, respectively. AccuiFRct was well correlated with FFR and iFR (correlation coefficients, 0.67 and 0.68, respectively). The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of AccuiFRct ≤ 0.89 for predicting FFR ≤ 0.80 were 78%, 73%, 81%, 73%, and 81%, respectively. Those of AccuiFRct ≤ 0.89 for predicting iFR ≤ 0.89 were 81%, 73%, 86%, 79%, and 82%, respectively. AccuiFRct showed a similar discriminant function when FFR or iFR were used as reference standards. CONCLUSION: AccuiFRct could be a promising noninvasive tool for detection of ischemia-causing coronary stenosis, as well as facilitating in making reliable clinical decisions.

Diagnostic value of 64-slice dual-source CT coronary angiography in patients with atrial fibrillation: comparison with invasive coronary angiography.

OBJECTIVE: We wanted to evaluate the image quality and diagnostic value of 64-slice dual-source computed tomography (DSCT) coronary angiography in patients with atrial fibrillation (Afib). MATERIALS AND METHODS: The coronary arteries of 22 Afib patients seen on DSCT were classified into 15 segments and the imaging quality (excellent, good, moderate and poor) and significant stenoses (≥ 50%) were evaluated by two radiologists who were blinded to the conventional coronary angiography (CAG) results. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for detecting important coronary artery stenosis were calculated. McNemar test was used to determine any significant difference between DSCT and CAG, and Cohen's Kappa statistics were calculated for the intermodality and interobserver agreement. RESULTS: The mean heart rate was 89 ± 8.3 bpm (range: 80-118 bpm). A range from 250 msec to 300 msec within the RR interval was the optimal reconstruction interval for the patients with Afib. The respective overall sensitivity, specificity, PPV and NPV values were 74%, 97%, 81% and 96% for reader 1 and 72%, 98%, 85% and 96% for reader 2. No significant difference between DSCT and CAG was found for detecting a significant stenosis (reader 1, p = 1.0; reader 2, p = 0.727). Cohen's Kappa statistics demonstrated good intermodality and interobserver agreement. CONCLUSION: 64-slice DSCT coronary angiography provides good image quality in patients with atrial fibrillation without the need for controlling the heart rate. DSCT can be used for ruling out significant stenosis in patients with atrial fibrillation with its high NPV for detecting in important stenosis.