Machine learning for nuclear cardiology: The way forward

[1] Cassar A(2009)Chronic coronary artery disease: Diagnosis and management Mayo Clin Proc 84 1130-1146

[2] Holmes DR(2007)Heterogeneity of atherosclerotic plaque characteristics in human coronary artery disease: A three-dimensional intravascular ultrasound study Catheter Cardiovasc Interv. 70 349-356

[3] Rihal CS(2018)Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning J Nucl Cardiol 40 887-894

[4] Gersh BJ(2015)The construction of support vector machine classifier using the firefly algorithm Comput Intell Neurosci. 22 877-884

[5] Box LC(2016)The importance of prediction model validation and assessment in obesity and nutrition research Int J Obes 20 553-562

[6] Angiolillo DJ(2018)Machine learning in cardiovascular medicine: Are we there yet? Heart 54 549-555

[7] Suzuki N(2015)Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population J Nucl Cardiol 44 2280-2289

[8] Box LA(2013)Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population J Nucl Cardiol 58 961-967

[9] Jian J(2017)Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning J Am Coll Cardiol Imaging. 17 405-413

[10] Guzman L(2013)Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease by utilizing a support vector machines algorithm J Nucl Cardiol 37 373-393

[1] Cassar A(2009)Chronic coronary artery disease: Diagnosis and management Mayo Clin Proc 84 1130-1146

[2] Holmes DR(2007)Heterogeneity of atherosclerotic plaque characteristics in human coronary artery disease: A three-dimensional intravascular ultrasound study Catheter Cardiovasc Interv. 70 349-356

[3] Rihal CS(2018)Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning J Nucl Cardiol 40 887-894

[4] Gersh BJ(2015)The construction of support vector machine classifier using the firefly algorithm Comput Intell Neurosci. 22 877-884

[5] Box LC(2016)The importance of prediction model validation and assessment in obesity and nutrition research Int J Obes 20 553-562

[6] Angiolillo DJ(2018)Machine learning in cardiovascular medicine: Are we there yet? Heart 54 549-555

[7] Suzuki N(2015)Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population J Nucl Cardiol 44 2280-2289

[8] Box LA(2013)Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population J Nucl Cardiol 58 961-967

[9] Jian J(2017)Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning J Am Coll Cardiol Imaging. 17 405-413

[10] Guzman L(2013)Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease by utilizing a support vector machines algorithm J Nucl Cardiol 37 373-393