Khalique Z, Pennell DJ. What is CMR doing for patients today? Eur Heart J. 2018;39(4):266–70. https://doi.org/10.1093/eurheartj/ehx778.

Article  PubMed  Google Scholar 

Pennell DJ, Sechtem UP, Higgins CB, Manning WJ, Pohost GM, Rademakers FE,…Yucel EK. Clinical indications for cardiovascular magnetic resonance (CMR): consensus panel report. Eur Heart J, 2004;25(21), 1940–1965. https://doi.org/10.1016/j.ehj.2004.06.040

Hillier E, Friedrich MG. The potential of oxygenation-sensitive CMR in Heart failure. Curr Heart Fail Rep. 2021. https://doi.org/10.1007/s11897-021-00525-y.

Article  PubMed  Google Scholar 

Fischer K, Yamaji K, Luescher S, Ueki Y, Jung B, von Tengg-Kobligk H, Windecker S, Friedrich MG, Eberle B, Guensch DP. Feasibility of cardiovascular magnetic resonance to detect oxygenation deficits in patients with multi-vessel coronary artery disease triggered by breathing maneuvers. J Cardiovasc Magn Reson. 2018;20(1):31. https://doi.org/10.1186/s12968-018-0446-y.PMID:29730991;PMCID:PMC5937049.

Article  PubMed  PubMed Central  Google Scholar 

Fischer K, Guensch DP, Jung B, King I, von Tengg-Kobligk H, Giannetti N, Eberle B, Friedrich MG. Insights into myocardial oxygenation and cardiovascular magnetic resonance tissue biomarkers in heart failure with preserved ejection fraction. Circ Heart Fail. 2022;15(4):e008903. https://doi.org/10.1161/CIRCHEARTFAILURE.121.008903.

Article  CAS  PubMed  Google Scholar 

Friedrich MG, Karamitsos TD. Oxygenation-sensitive cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2013;15(1):43. https://doi.org/10.1186/1532-429X-15-43.PMID:23706167;PMCID:PMC3681671.

Article  PubMed  PubMed Central  Google Scholar 

Dharmakumar R, Qi X, Hong J, Wright GA. Detecting microcirculatory changes in blood oxygen state with steady-state free precession imaging. Magn Reson Med. 2006;55(6):1372–80. https://doi.org/10.1002/mrm.20911.

Article  PubMed  Google Scholar 

Hillier E, Covone J, Friedrich MG. Oxygenation-sensitive cardiac MRI with vasoactive breathing maneuvers for the non-invasive assessment of coronary microvascular dysfunction. JoVE (Journal of Visualized Experiments). 2022;186:e64149. https://doi.org/10.3791/64149.

Article  Google Scholar 

Leiner T, Rueckert D, Suinesiaputra A, Baeßler B, Nezafat R, Išgum I, Young AA. Machine learning in cardiovascular magnetic resonance: basic concepts and applications. J Cardiovasc Magn Reson. 2019;21(1):61. https://doi.org/10.1186/s12968-019-0575-y.

Article  PubMed  PubMed Central  Google Scholar 

Fotaki A, Puyol-Antón E, Chiribiri A, Botnar R, Pushparajah K, Prieto C. Artificial intelligence in cardiac MRI: is clinical adoption forthcoming? Front Cardiovasc Med. 2022;8:818765. https://doi.org/10.3389/fcvm.2021.818765.

Article  PubMed  PubMed Central  Google Scholar 

Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang G-Z. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21(1):4–21. https://doi.org/10.1109/JBHI.2016.2636665.

Article  PubMed  Google Scholar 

Baeßler B, Weiss K, Pinto Dos Santos D. Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol. 2019;54(4):221–8. https://doi.org/10.1097/RLI.0000000000000530.

Article  PubMed  Google Scholar 

Neisius U, El-Rewaidy H, Nakamori S, Rodriguez J, Manning WJ, Nezafat R. Radiomic analysis of myocardial native T1 imaging discriminates between hypertensive heart disease and hypertrophic cardiomyopathy. JACC: Cardiovasc Imaging. 2019;12(10):1946–54.

PubMed  Google Scholar 

Cerqueira MD, Weissman NJ, Dilsizian V, Jacobs AK, Kaul S, Laskey WK,...& American Heart Association Writing Group on Myocardial Segmentation and Registration for Cardiac Imaging. Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation, 2022;105(4), 539-542

Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information. Phys Rev E. 2004;69(6):066138. https://doi.org/10.1103/PhysRevE.69.066138.

Article  CAS  Google Scholar 

Chen YW, & Lin CJ (2006) Combining SVMs with various feature selection strategies. In I. Guyon, M. Nikravesh, S. Gunn, & L. A. Zadeh (Eds.), Feature Extraction (Vol. 207, pp. 315–324). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_13

Spencer R, Thabtah F, Abdelhamid N, Thompson M. Exploring feature selection and classification methods for predicting heart disease. Digital Health. 2020;6:2055207620914777. https://doi.org/10.1177/2055207620914777.

Article  PubMed  PubMed Central  Google Scholar 

Pal M, Parija S. Prediction of heart diseases using random forest. J Phys: Conf Ser. 2021;1817(1):012009. https://doi.org/10.1088/1742-6596/1817/1/012009.

Article  Google Scholar 

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, van Mulbregt P. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods. 2020;17(3):261–72. https://doi.org/10.1038/s41592-019-0686-2.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O,…Cournapeau D. (n.d.) Scikit-learn: machine learning in Python. MACHINE LEARNING IN PYTHON.

Hillier E, Plasa G, Luu J, Benovoy M, Friedrich M G. Machine learning analysis of oxygenation-sensitive cardiovascular magnetic resonance imaging in patients with coronary artery stenosis. Society of Cardiovascular Magnetic Resonance Imaging. 24–26. San Diego, California. USA: Poster Presentation; 2023.

Google Scholar 

Ghantous CM, Kamareddine L, Farhat R, Zouein FA, Mondello S, Kobeissy F, Zeidan A. Advances in cardiovascular biomarker discovery. Biomedicines. 2020;8(12):552. https://doi.org/10.3390/biomedicines8120552.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hathaway QA, Roth SM, Pinti MV, Sprando DC, Kunovac A, Durr AJ,…Hollander JM (2019) Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics. Cardiovasc Diabetol, 18(1), 78. https://doi.org/10.1186/s12933-019-0879-0

Jack CR, Bennett DA, Blennow K, Carrillo MC, Feldman HH, Frisoni GB,…Dubois B (2016) A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology, 87(5), 539–547. https://doi.org/10.1212/WNL.0000000000002923