Scientific paper - Original scientific paper
Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience
Cardiovascular Diagnosis and Therapy, 10 (2020), 4; 820-830. https://doi.org/10.21037/cdt-20-381

Eberhard, Matthias; Nadarevic, Tin; Cousin, Andrej; von Spiczak, Jochen; Hinzpeter, Ricarda; Euler, Andre; Morsbach, Fabian; Manka, Robert; Keller, Dagmar I.; Alkadhi, Hatem

Cite this document

Eberhard, M., Nadarevic, T., Cousin, A., von Spiczak, J., Hinzpeter, R., Euler, A. ... Alkadhi, H. (2020). Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience. Cardiovascular Diagnosis and Therapy, 10. (4), 820-830. doi: 10.21037/cdt-20-381

Eberhard, Matthias, et al. "Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience." Cardiovascular Diagnosis and Therapy, vol. 10, no. 4, 2020, pp. 820-830. https://doi.org/10.21037/cdt-20-381

Eberhard, Matthias, Tin Nadarevic, Andrej Cousin, Jochen von Spiczak, Ricarda Hinzpeter, Andre Euler, Fabian Morsbach, Robert Manka, Dagmar I. Keller and Hatem Alkadhi. "Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience." Cardiovascular Diagnosis and Therapy 10, no. 4 (2020): 820-830. https://doi.org/10.21037/cdt-20-381

Eberhard, M., et al. (2020) 'Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience', Cardiovascular Diagnosis and Therapy, 10(4), pp. 820-830. doi: 10.21037/cdt-20-381

Eberhard M, Nadarevic T, Cousin A, von Spiczak J, Hinzpeter R, Euler A, and sur.. Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience. Cardiovascular Diagnosis and Therapy [Internet]. 2020 [cited 2024 July 26];10(4):820-830. doi: 10.21037/cdt-20-381

M. Eberhard, et al., "Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience", Cardiovascular Diagnosis and Therapy, vol. 10, no. 4, pp. 820-830, 2020. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:184:552413. [Accessed: 26 July 2024]

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