scholarly journals Additional Value of Machine-Learning Computed Tomographic Angiography-Based Fractional Flow Reserve Compared to Standard Computed Tomographic Angiography

2020 ◽  
Vol 9 (3) ◽  
pp. 676
Author(s):  
Dirk Lossnitzer ◽  
Leonard Chandra ◽  
Marlon Rutsch ◽  
Tobias Becher ◽  
Daniel Overhoff ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Computed Tomographic ◽  
Coronary Syndrome ◽  
Flow Reserve ◽  
Tomographic Angiography

Background: Machine-learning-based computed-tomography-derived fractional flow reserve (CT-FFRML) obtains a hemodynamic index in coronary arteries. We examined whether it could reduce the number of invasive coronary angiographies (ICA) showing no obstructive lesions. We further compared CT-FFRML-derived measurements to clinical and CT-derived scores. Methods: We retrospectively selected 88 patients (63 ± 11years, 74% male) with chronic coronary syndrome (CCS) who underwent clinically indicated coronary computed tomography angiography (cCTA) and ICA. cCTA image data were processed with an on-site prototype CT-FFRML software. Results: CT-FFRML revealed an index of >0.80 in coronary vessels of 48 (55%) patients. This finding was corroborated in 45 (94%) patients by ICA, yet three (6%) received revascularization. In patients with an index ≤ 0.80, three (8%) of 40 were identified as false positive. A total of 48 (55%) patients could have been retained from ICA. CT-FFRML (AUC = 0.96, p ≤ 0.0001) demonstrated a higher diagnostic accuracy compared to the pretest probability or CT-derived scores and showed an excellent sensitivity (93%), specificity (94%), positive predictive value (PPV; 93%) and negative predictive value (NPV; 94%). Conclusion: CT-FFRML could be beneficial for clinical practice, as it may identify patients with CAD without hemodynamical significant stenosis, and may thus reduce the rate of ICA without necessity for coronary intervention.

Additional value of deep learning computed tomographic angiography-based fractional flow reserve in detecting coronary stenosis and predicting outcomes

2021 ◽  
pp. 028418512098397
Author(s):  
Yang Li ◽  
Hong Qiu ◽  
Zhihui Hou ◽  
Jianfeng Zheng ◽  
Jianan Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Unstable Angina ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Computed Tomographic ◽  
Flow Reserve ◽  
Sensitivity Specificity ◽  
Tomographic Angiography

Background Deep learning (DL) has achieved great success in medical imaging and could be utilized for the non-invasive calculation of fractional flow reserve (FFR) from coronary computed tomographic angiography (CCTA) (CT-FFR). Purpose To examine the ability of a DL-based CT-FFR in detecting hemodynamic changes of stenosis. Material and Methods This study included 73 patients (85 vessels) who were suspected of coronary artery disease (CAD) and received CCTA followed by invasive FFR measurements within 90 days. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristics curve (AUC) were compared between CT-FFR and CCTA. Thirty-nine patients who received drug therapy instead of revascularization were followed for up to 31 months. Major adverse cardiac events (MACE), unstable angina, and rehospitalization were evaluated and compared between the study groups. Results At the patient level, CT-FFR achieved 90.4%, 93.6%, 88.1%, 85.3%, and 94.9% in accuracy, sensitivity, specificity, PPV, and NPV, respectively. At the vessel level, CT-FFR achieved 91.8%, 93.9%, 90.4%, 86.1%, and 95.9%, respectively. CT-FFR exceeded CCTA in these measurements at both levels. The vessel-level AUC for CT-FFR also outperformed that for CCTA (0.957 vs. 0.599, P < 0.0001). Patients with CT-FFR ≤0.8 had higher rates of rehospitalization (hazard ratio [HR] 4.51, 95% confidence interval [CI] 1.08–18.9) and MACE (HR 7.26, 95% CI 0.88–59.8), as well as a lower rate of unstable angina (HR 0.46, 95% CI 0.07–2.91). Conclusion CT-FFR is superior to conventional CCTA in differentiating functional myocardial ischemia. In addition, it has the potential to differentiate prognoses of patients with CAD.

Flow resistance coefficient analysis of left anterior descending artery stenosis: A preliminary study

2019 ◽  
Vol 234 (1) ◽  
pp. 100-109
Author(s):  
Jian Liu ◽  
Fan Yu ◽  
Yu Zhang
Keyword(s):  
Fluid Dynamics ◽  
Fractional Flow Reserve ◽  
Flow Resistance ◽  
Resistance Coefficient ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Computed Tomographic ◽  
Non Invasive ◽  
Flow Reserve ◽  
Tomographic Angiography

As a non-invasive diagnosis method, computed tomographic angiography–based computational fluid dynamics is used to estimate fractional flow reserve of coronary arteries (FFRct). However, FFRct uses hypothetical hemodynamic flow conditions, and thus FFRct might cause mismatches (fractional flow reserve > 0.8) with invasive fractional flow reserve (≤0.8). Additional computational fluid dynamics–based criteria are still needed to improve the accuracy of non-invasive diagnosis. In this article, a new concept of computed tomographic angiography–based flow resistance coefficient (FRCct) is proposed, and it tests pressure drops at coronary arteries under different blood flow rates and returns two constant flow resistance coefficients (A and B) for each artery. Specifically, 30 patients who were suspected to meet the treatment indication of their left anterior descending stenosis were tested with invasive fractional flow reserve and FRCct. The invasive fractional flow reserve divided the patients into a safe group (invasive fractional flow reserve > 0.8, 15 patients) and a sick group (invasive fractional flow reserve ≤ 0.8, 15 patients). A following FRCct indicated that the flow resistance coefficient always displayed a low value (A = 0.0039 ± 2.7e–5; B = 0.079 ± 0.0025) for the safe group, while the flow resistance coefficient always exhibited a high value (A = 0.0235 ± 0.001; B = 0.270 ± 0.108) for the sick group. The results of the statistical test indicated that the p-value was less than 0.05 for both A and B of the two groups. In conclusion, in addition to the FFRct, FRCct is a supplementary non-invasive method to evaluate the treatment indication of left anterior descending stenosis.

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