Key result
DEEPVESSEL-FFR demonstrated higher diagnostic performance in determining ischemia-related lesions with an AUC of 0.928 compared to 0.664 for CTA stenotic severity.
Why the study?
To evaluate the DEEPVESSEL-FFR platform using a deep learning technique to calculate FFR values from coronary CTA images as an efficient method.
Does the DEEPVESSEL-FFR deep learning platform accurately calculate FFR from coronary CT angiography compared to wire-based FFR in patients with stenotic coronary arteries?
Observational (n=63)
Single-blind
No
Does the DEEPVESSEL-FFR deep learning platform accurately calculate FFR from coronary CT angiography compared to wire-based FFR in patients with stenotic coronary arteries?
The DEEPVESSEL-FFR deep learning platform demonstrates high sensitivity and negative predictive value for non-invasively assessing the functional significance of coronary stenosis compared to invasive wire-based FFR.
May enable faster noninvasive FFR estimation from CTA; leaves open accuracy and outcome impact in prospective validation.
BACKGROUND: The computational fluid dynamics (CFD) approach has been frequently applied to compute the fractional flow reserve (FFR) using computed tomography angiography (CTA). This technique is efficient. We developed the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value out of CTA images in five minutes. This study is to evaluate the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value from CTA images as an efficient method. METHODS: A single-center, prospective study was conducted and 63 patients were enrolled for the evaluation of the diagnostic performance of DEEPVESSEL-FFR. Automatic quantification method for the three-dimensional coronary arterial geometry and the deep learning based prediction of FFR were developed to assess the ischemic risk of the stenotic coronary arteries. Diagnostic performance of the DEEPVESSEL-FFR was assessed by using wire-based FFR as reference standard. The primary evaluation factor was defined by using the area under receiver-operation characteristics curve (AUC) analysis. The secondary evaluation factors, indicating per vessel accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 87.3%, 97.14%, 75%, 82.93%, and 95.45%, respectively. CONCLUSION: DEEPVESSEL-FFR is a novel method that allows efficient assessment of the functional significance of coronary stenosis.
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Zhao et al. (2019) conducted an observational in Coronary artery disease (n=63). DEEPVESSEL-FFR vs. CTA stenotic severity was evaluated on Diagnostic performance (Area Under the Curve) for determining ischemia-related lesions (FFR ≤ 0.8) on a per-patient basis (AUC 0.928, 95% CI 0.833-0.978). DEEPVESSEL-FFR demonstrated higher diagnostic performance in determining ischemia-related lesions with an AUC of 0.928 compared to 0.664 for CTA stenotic severity.
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