Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review.
J Am Coll Cardiol. 73: 1317-1335Obermeyer Z. Emanuel E.J.Predicting the future - big data, machine learning, and clinical medicine.
N Engl J Med. 375: 1216-1219Segar M.W. Patel K.V. Ayers C. et al.Phenomapping of patients with heart failure with preserved ejection fraction using machine learning-based unsupervised cluster analysis.
Eur J Heart Fail. 22: 148-158Krittanawong C. Tunhasiriwet A. Zhang H. et al.Deep learning with unsupervised feature in echocardiographic imaging.
J Am Coll Cardiol. 69: 2100-2101Betancur J. Commandeur F. Motlagh M. et al.Deep learning for prediction of obstructive disease from fast myocardial perfusion SPECT: a multicenter study.
JACC Cardiovascular imaging. 11: 1654-1663Betancur J. Hu L.H. Commandeur F. et al.Deep learning analysis of upright-supine high-efficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study.
J Nucl Med. 60: 664-670Lundervold A.S. Lundervold A.An overview of deep learning in medical imaging focusing on MRI.
Z Med Phys. 29: 102-127Ramon A.J. Yang Y. Pretorius P.H. et al.Improving diagnostic accuracy in low-dose SPECT myocardial perfusion imaging with convolutional denoising networks.
IEEE Trans Med imaging. 39: 2893-2903Ronneberger O. Fischer P. Brox T.U-net: convolutional networks for biomedical image segmentation.
MICCAI. : 234-241A K-fold averaging cross-validation procedure.
J Nonparametr Stat. 27: 167-179Cawley G.C. Talbot N.L.C.On over-fitting in model selection and subsequent selection bias in performance evaluation.
J Machine Learn Res. 11: 2079-2107Riley R.D. Ensor J. Snell K.I. et al.External validation of clinical prediction models using big datasets from e-health records or IPD meta-analysis: opportunities and challenges.
BMJ. 353: i3140Miller R.J.H. Sabovčik F. Cauwenberghs N. et al.Temporal shift and predictive performance of machine learning for heart transplant outcomes.
J Heart Lung Transplant. 41: 928-936Miller R.J.H., Rozanski A., Slomka P.J., et al., Development and validation of ischemia risk scores, J Nucl Cardiol, 2022. doi: 10.1007/s12350-022-02976-9. Online ahead of print.
Van Calster B. Vickers A.J.Calibration of risk prediction models: impact on decision-analytic performance.
Med Decis Making. 35: 162-169Ramon A.J. Yang Y. Pretorius P.H. et al.Initial investigation of low-dose SPECT-MPI via deep learning.
IEEE Nucl Sci Symp. : 1-3Song C. Yang Y. Wernick M.N. et al.Low-dose cardiac-gated spect studies using a residual convolutional neural network.
IEEE Int Symp Biomed Imaging. 1: 653-656Wang B. and Liu H., FBP-Net for direct reconstruction of dynamic PET images, Phys Med Biol, 65 (23), 2020, 1-16.
Shiri I. AmirMozafari Sabet K. Arabi H. et al.Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks.
J Nucl Cardiol. 28: 2761-2779Dorbala S. Di Carli M.F. Delbeke D. et al.SNMMI/ASNC/SCCT guideline for cardiac SPECT/CT and PET/CT 1.0.
J Nucl Med. 54: 1485-1507Arsanjani R. Xu Y. Hayes S.W. et al.Comparison of fully automated computer analysis and visual scoring for detection of coronary artery disease from myocardial perfusion SPECT in a large population.
J Nucl Med. 54: 221-228Prevalence of misregistration between SPECT and CT for attenuation-corrected myocardial perfusion SPECT.
J Nucl Cardiol. 14: 200-206Ko C.-L. Cheng M.-F. Yen R.-F. et al.Automatic alignment of CZT myocardial perfusion SPECT and external non-contrast CT by deep-learning model and dynamic data generation.
J Nucl Med. 60: 570Shi L. Onofrey J.A. Liu H. et al.Deep learning-based attenuation map generation for myocardial perfusion SPECT.
Eur J Nucl Med Mol Imaging. 47: 2383-2395Li T. Zhang M. Qi W. et al.Motion correction of respiratory-gated PET images using deep learning based image registration framework.
Phys Med Biol. 65: 155003Guo X. Zhou B. Pigg D. et al.Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network.
Med Image Anal. 80: 102524Shi L. Lu Y. Dvornek N. et al.Automatic inter-frame patient motion correction for dynamic cardiac PET using deep learning.
IEEE Trans Med Imaging. 40: 3293-3304Hagio T. Poitrasson-Rivière A. Moody J.B. et al.Virtual” attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning.
Eur J Nucl Med Mol Imaging. 49: 3140-3149Chen X.C. Zhou B. Shi L.Y. et al.CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.
J Nucl Cardiol. 29: 2235-2250Shanbhag A.D. Miller R.J.H. Pieszko K. et al.Deep learning-based attenuation correction improves diagnostic accuracy of cardiac SPECT.
J Nucl Med. jnumed: 264429Trpkov C. Savtchenko A. Liang Z. et al.Visually estimated coronary artery calcium score improves SPECT-MPI risk stratification.
Int J Cardiol Heart Vasc. 35: 100827Wolterink J.M. Leiner T. de Vos B.D. et al.Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.
Med Image Anal. 34: 123-136Takx R.A. de Jong P.A. Leiner T. et al.Automated coronary artery calcification scoring in non-gated chest CT: agreement and reliability.
PLoS One. 9: e91239Zeleznik R. Foldyna B. Eslami P. et al.Deep convolutional neural networks to predict cardiovascular risk from computed tomography.
Nat Commun. 12: 715Isgum I. de Vos B.D. Wolterink J.M. et al.Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT.
J Nucl Cardiol. 25: 2133-2142Pieszko K. Shanbhag A.D. Lemley M. et al.Reproducibility of quantitative coronary calcium scoring from PET/CT attenuation maps: comparison to ECG-gated CT scans.
Eur J Nucl Med Mol Imaging. 49: 4122-4132Pieszko K. Shanbhag A. Killekar A. et al.Deep learning of coronary calcium scores from PET/CT attenuation maps accurately predicts adverse cardiovascular events.
JACC Cardiovasc Imaging. ()Miller R.J.H. Pieszko K. Shanbhag A. et al.Deep learning coronary artery calcium scores from SPECT/CT attenuation maps improves prediction of major adverse cardiac events.
J Nucl Med. jnumed: 264423Eisenberg E. McElhinney P.A. Commandeur F. et al.Deep learning-based quantification of epicardial adipose tissue volume and attenuation predicts major adverse cardiovascular events in asymptomatic subjects.
Circ Cardiovasc Imaging. 13: e009829Arsanjani R. Xu Y. Dey D. et al.Improved accuracy of myocardial perfusion SPECT for the detection of coronary artery disease using a support vector machine algorithm.
J Nucl Med. 54: 549-555Eisenberg E. Miller R.J.H. Hu L.H. et al.Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT.
J Nucl Cardiol. 29: 2295-2307Miller R.J.H. Hauser M.T. Sharir T. et al.Machine learning to predict abnormal myocardial perfusion from pre-test features.
J Nucl Cardiol. 29: 2393-2403Spier N. Nekolla S. Rupprecht C. et al.Classification of polar maps from cardiac perfusion imaging with graph-convolutional neural networks.
Sci Rep. 9: 7569Otaki Y. Singh A. Kavanagh P. et al.Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease.
JACC Cardiovasc Imaging. 15: 1091-1102Miller R.J.H. Singh A. Otaki Y. et al.Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images.
Eur J Nucl Med Mol Imaging. ()Togo R. Hirata K. Manabe O. et al.Cardiac sarcoidosis classification with deep convolutional neural network-based features using polar maps.
Comput Biol Med. 104: 81-86Halme H.L. Ihalainen T. Suomalainen O. et al.Convolutional neural networks for detection of transthyretin amyloidosis in 2D scintigraphy images.
EJNMMI Res. 12: 27Betancur J. Otaki Y. Motwani M. et al.Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning.
JACC Cardiovasc Imaging. 11: 1000-1009Hu L.H. Betancur J. Sharir T. et al.Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.
Eur Heart J Cardiovasc Imaging. 21: 549-559Hu L.H. Miller R.J.H. Sharir T. et al.Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT.
Eur Heart J Cardiovasc Imaging. 22: 705-714Juarez-Orozco L.E. Martinez-Manzanera O. van der Zant F.M. et al.Deep learning in quantitative PET myocardial perfusion imaging: a study on cardiovascular event prediction.
JACC Cardiovasc Imaging. 13: 180-182Singh A. Kwiecinski J. Miller R.J.H. et al.Deep learning for explainable estimation of mortality risk from myocardial positron emission tomography images.
Circ Cardiovasc Imaging. 15: e014526Singh A. Miller R.J.H. Otaki Y. et al.Direct risk assessment from myocardial perfusion imaging using explainable deep learning.
JACC Cardiovasc Imaging. S1936-878X: 00484-00493Haro Alonso D. Wernick M.N. Yang Y. et al.Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning.
J Nucl Cardiol. 26: 1746-1754Rios R. Miller R.J.H. Hu L.H. et al.Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry.
Cardiovasc Res. 118: 2152-2164Rios R. Miller R.J.H. Manral N. et al.Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: insights from REFINE SPECT registry.
Comput Biol Med. 145: 105449Selvaraju R.R. Cogswell M. Das A. et al.Grad-cam: visual explanations from deep networks via gradient-based localization.
IEEE Int Conf Comput Vis. : 618-626Miller R.J.H. Kuronuma K. Singh A. et al.Explainable deep learning improves physician interpretation of myocardial perfusion imaging.
J Nucl Med. 63: 1768-1774
留言 (0)