U. Schoepf to Machine Learning
This is a "connection" page, showing publications U. Schoepf has written about Machine Learning.
Connection Strength
4.766
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Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations. J Thorac Imaging. 2020 May; 35 Suppl 1:S21-S27.
Score: 0.600
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Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry. JACC Cardiovasc Imaging. 2020 03; 13(3):760-770.
Score: 0.571
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Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling. Radiology. 2018 Jul; 288(1):64-72.
Score: 0.521
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Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve Based on Machine Learning for Risk Stratification of Non-Culprit Coronary Narrowings in Patients with Acute Coronary Syndrome. Am J Cardiol. 2017 Oct 15; 120(8):1260-1266.
Score: 0.496
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Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia. BMC Cardiovasc Disord. 2022 02 05; 22(1):34.
Score: 0.170
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Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography. J Cardiovasc Comput Tomogr. 2021 Nov-Dec; 15(6):492-498.
Score: 0.162
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Machine Learning and Coronary Artery Calcium Scoring. Curr Cardiol Rep. 2020 07 09; 22(9):90.
Score: 0.152
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Ischemia and outcome prediction by cardiac CT based machine learning. Int J Cardiovasc Imaging. 2020 Dec; 36(12):2429-2439.
Score: 0.152
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Impact of machine learning-based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease. Eur Radiol. 2020 Nov; 30(11):5841-5851.
Score: 0.151
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Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis. Clin Res Cardiol. 2020 Jun; 109(6):735-745.
Score: 0.145
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Diagnostic Performance of Machine Learning Based CT-FFR in Detecting Ischemia in Myocardial Bridging and Concomitant Proximal Atherosclerotic Disease. Can J Cardiol. 2019 11; 35(11):1523-1533.
Score: 0.143
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Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome. Am J Cardiol. 2019 11 01; 124(9):1340-1348.
Score: 0.143
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Oracle of Our Time: Machine Learning for Predicting Cardiovascular Events. Radiology. 2019 Aug; 292(2):363-364.
Score: 0.141
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Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2019 06 21; 40(24):1975-1986.
Score: 0.141
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Effect of Tube Voltage on Diagnostic Performance of Fractional Flow Reserve Derived From Coronary CT Angiography With Machine Learning: Results From the MACHINE Registry. AJR Am J Roentgenol. 2019 08; 213(2):325-331.
Score: 0.140
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Machine Learning Using CT-FFR Predicts Proximal Atherosclerotic Plaque Formation Associated With LAD Myocardial Bridging. JACC Cardiovasc Imaging. 2019 08; 12(8 Pt 1):1591-1593.
Score: 0.139
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Coronary CT angiography-derived plaque quantification with artificial intelligence CT fractional flow reserve for the identification of lesion-specific ischemia. Eur Radiol. 2019 May; 29(5):2378-2387.
Score: 0.136
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Artificial intelligence machine learning-based coronary CT fractional flow reserve (CT-FFRML): Impact of iterative and filtered back projection reconstruction techniques. J Cardiovasc Comput Tomogr. 2019 Nov - Dec; 13(6):331-335.
Score: 0.135
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The power and limitations of machine learning and artificial intelligence in cardiac CT. J Cardiovasc Comput Tomogr. 2018 May - Jun; 12(3):202-203.
Score: 0.131
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Coronary CT Angiography-derived Fractional Flow Reserve. Radiology. 2017 10; 285(1):17-33.
Score: 0.126
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Machine Learning for the Prevalence and Severity of Coronary Artery Calcification in Nondialysis Chronic Kidney Disease Patients: A Chinese Large Cohort Study. J Thorac Imaging. 2022 Nov 01; 37(6):401-408.
Score: 0.043
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Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. Eur Radiol. 2022 Sep; 32(9):6008-6016.
Score: 0.043
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One-year outcomes of CCTA alone versus machine learning-based FFRCT for coronary artery disease: a single-center, prospective study. Eur Radiol. 2022 Aug; 32(8):5179-5188.
Score: 0.043
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Improved long-term prognostic value of coronary CT angiography-derived plaque measures and clinical parameters on adverse cardiac outcome using machine learning. Eur Radiol. 2021 Jan; 31(1):486-493.
Score: 0.038
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Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry. Eur J Radiol. 2019 Oct; 119:108657.
Score: 0.036
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Diagnostic performance of fractional flow reserve derived from coronary CT angiography for detection of lesion-specific ischemia: A multi-center study and meta-analysis. Eur J Radiol. 2019 Jul; 116:90-97.
Score: 0.035
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Comparison of the Diagnostic Performance of Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve in Patients With Versus Without Diabetes Mellitus (from the MACHINE Consortium). Am J Cardiol. 2019 02 15; 123(4):537-543.
Score: 0.034