S. S. Virani et al., “Heart disease and stroke statistics—2020 update: a report from the American Heart Association”. Circulation, 141:139–596, 2020.

M. Urbich et al., “A Systematic Review of Medical Costs Associated with Heart Failure in the USA (2014–2020)”. Pharmacoeconomics, 38:1219–1236, 2020.

A. Auricchio et al., “Long-term clinical effect of hemodynamically optimized cardiac resynchronization therapy in patients with heart failure and ventricular conduction delay”. JACC, 39:2026–2033, 2002.

A. Auricchio and F. W. Prinzen, “Non-Responders to Cardiac Resynchronization Therapy–The Magnitude of the Problem and the Issues–”. Circ J, 75:521–527, 2011.

E. Donal et al., “New multiparametric analysis of cardiac dyssynchrony: machine learning and prediction of response to CRT”. JACC: Cardiovasc. Imaging, 12:1887–1888, 2019.

W. Zhou and E. V. Garcia, “Nuclear Image-Guided Approaches for Cardiac Resynchronization Therapy (CRT)”. Curr Cardiol Rep, 18:1–11, 2016.

Z. He, E. V. Garcia, and W. Zhou, “Nuclear Image-Guided Methods for Cardiac Resynchronization Therapy”. J Nucl Cardiol, https://doi.org/10.1007/978-3-030-62195-7_25, 2021.

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning”. Electronic Markets, 31:685–695, 2021.

M. Tokodi et al., “Machine Learning-Based Mortality Prediction of Patients Undergoing Cardiac Resynchronization Therapy: The SEMMELWEIS-CRT Score”. Eur Heart J, 41:1747–1756, 2020.

A. K. Feeny et al., “Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.” Circ Arrhythm Electrophysiol, https://doi.org/10.1161/CIRCEP.119.007316, 2019.

M. M. Kalscheur et al., “Machine learning algorithm predicts cardiac resynchronization therapy outcomes: lessons from the COMPANION trial”. Circ Arrhythm Electrophysiol , https://doi.org/10.1161/CIRCEP.117.005499, 2018.

F. de A. Fernandes et al., “A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support”. Eur J Nucl Med Mol Imaging, 50:3022–3033, 2023.

Howell et al., “Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: the SMART-AV Study”. JACC Clin Electrophysiol, 7: 1505-1515, 2021.

Manohar et al., “Prediction of Cardiac Resynchronization Therapy Response Using a Lead Placement Score Derived From 4-Dimensional Computed Tomography”. Circ Cardiovasc Imaging, https://doi.org/10.1161/circimaging.122.014165.

Chang et al., “Deep Learning Significantly Boosts CRT Response Prediction Using Synthetic Longitudinal Strain Data: Training on Synthetic Data and Testing on Real Patients”. Biomed. J., https://doi.org/10.1016/j.bj.2024.100803.

Larsen et al., “A New Method Using Deep Learning to Preidct the Response to Cardiac Resynchronization Therapy”. J Imaging Inform Med, https://doi.org/10.1007/s10278-024-01380-8.

Nejadeh et al., “Predicting the response to cardiac resynchronization therapy (CRT) using the deep learning approach”. Biocybern. Biomed Eng., 41: 758-778, 2021.

Khamzin et al., “Machine Learning Prediction of Cardiac Resynchronization Therapy Response From Combination of Clinical and Model-Driven Data”. Front Physio., https://doi.org/10.3389/fphys.2021.753282.

Haque et al., “Interpretable machine learning predicts cardiac resynchronization therapy responses from personalized biochemical and biomechanical features”. BMC Med. Inform. Decis. Mak., https://doi.org/10.1186/s12911-022-02015-0.

A. Peix et al., “Value of intraventricular dyssynchrony assessment by gated-SPECT myocardial perfusion imaging in the management of heart failure patients undergoing cardiac resynchronization therapy (VISION-CRT)”. J Nucl Cardiol, 28:55–64, 2021.

J. Zou et al., “SPECT-guided LV lead placement for incremental CRT efficacy: validated by a prospective, randomized, controlled study”. JACC: Cardiovasc. Imaging, 12:2580–2583, 2019.

G.-U. Hung et al., “Left-ventricular dyssynchrony in viable myocardium by myocardial perfusion SPECT is predictive of mechanical response to CRT”. Ann Nucl Med, 35:947–954, 2021.

M. J. Boogers et al., “Optimal left ventricular lead position assessed with phase analysis on gated myocardial perfusion SPECT”. Eur J Nucl Med Mol Imaging, 38:230–238, 2011.

W. Zhou et al., “Development and validation of an automatic method to detect the latest contracting viable left ventricular segments to assist guide CRT therapy from gated SPECT myocardial perfusion imaging”. J Nucl Cardiol, 25: 1948–1957, 2018.

E. Begoli, T. Bhattacharya, and D. Kusnezov, “The need for uncertainty quantification in machine-assisted medical decision making”. Nature Machine Intelligence, 1:20–23, 2019.

Y. Kwon, J.-H. Won, B. J. Kim, and M. C. Paik, “Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation”. Computational Statistics Data Analysis, https://doi.org/10.1016/j.csda.2019.106816, 2020.

J. Linmans, S. Elfwing, J. van der Laak, and G. Litjens, “Predictive uncertainty estimation for out-of-distribution detection in digital pathology”. Medical Image Analysis, https://doi.org/10.1016/j.media.2022.102655, 2022.

E. Heid, C. J. McGill, F. H. Vermeire, and W. H. Green, “Characterizing uncertainty in machine learning for chemistry”. Journal of Chemical Information and Modeling, 63:4012–4029, 2023.

D. Morgan and R. Jacobs, “Opportunities and challenges for machine learning in materials science”. Annual Review of Materials Research, 50:71–103, 2020.

Pedregosa et al., “Scikit-learn: Machine Learning in Python”. JMLR, 12: 2825-2830, 2011.

C. M. Tracy, “2012 ACCF/AHA/HRS Focused Update of the 2008 Guidelines for Device-Based Therapy of Cardiac Rhythm Abnormalities: A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines”. JACC, 60: 2604–2605, 2012.

M. Fudim and S. Borges-Neto, “A troubled marriage: When electrical and mechanical dyssynchrony don’t go along”. J Nucl Cardiol, 26: 1240–1242, 2019.

I. Lozano et al., “Impact of biventricular pacing on mortality in a randomized crossover study of patients with heart failure and ventricular arrhythmias”. Pacing and Clinical Electrophysiology, 23:1711–1712, 2000.

C.-M. Yu et al., “Tissue Doppler velocity is superior to displacement and strain mapping in predicting left ventricular reverse remodelling response after cardiac resynchronisation therapy”. Heart, 92: 1452–1456, 2006.

T. Abraham et al., “Imaging cardiac resynchronization therapy”. JACC: Cardiovasc. Imaging, 2:486–497, 2009.

W. AlJaroudi, J. Chen, W. A. Jaber, S. G. Lloyd, M. D. Cerqueira, and T. Marwick, “Nonechocardiographic imaging in evaluation for cardiac resynchronization therapy”. Circ Cardiovascr Imaging, 4:334–343, 2011.

P. Carita et al., “Non-responders to cardiac resynchronization therapy:` Insights from multimodality imaging and electrocardiography. A brief review”. Int J Cardiol Cardiovasc Dis, 225:402–407, 2016.

Linde et al., “Long-term benefits of biventricular pacing in congestive heart failure: results from the Multisite Stimulation in cardiomyopathy (MUSTIC) study”. JACC, 40: 111-118, 2002.

Moss et al., “Cardiac-Resynchronization Therapy for the Prevention of Heart-Failure Events”. NEJM, 361: 1329-1338, 2009.

Gold et al., “Effect of QRS Duration and Morphology on Cardiac Resynchronization Therapy Outcomes in Mild Heart Failure: Results from the Resynchronization Reverses Remodeling in Systolic Left Ventricular Dysfunction (REVERSE) study”. Circ, 126: 822-829, 2012.

Sassone et al., “Relation of QRS duration to response to cardiac resynchronization therapy”. Am J Cardiol, 115: 214-219, 2014.

S. Y. Naqvi, A. Jawaid, I. Goldenberg, and V. Kutyifa, “Non-response to cardiac resynchronization therapy”. Current heart failure reports, 15:315–321, 2018.

Chen J, Faber TL, Cooke CD, Garcia EV. Temporal resolution of multi-harmonic phase analysis of ECGgated myocardial perfusion SPECT studies. J Nucl Cardiol 2008;15:383–91. [PMID: 18513645] [PMCID: PMC2992837].

Trimble MA, Velazquez EJ, Adams GL, Honeycutt EF, Pagnanelli RA, Barnhart HX, Chen J, Iskandrian AE, Garcia EV, Borges-Neto S. Repeatability and reproducibility of phase analysis of gated SPECT myocardial perfusion imaging used to quantify cardiac dyssynchrony. Nucl Med Commun 2008;29:374–81. [PMID: 18317303] [PMCID: PMC3048057].

Lin X, Xu H, Zhao X, Folks RD, Faber TL, Garcia EV, Chen J. Repeatability of left ventricular dyssynchrony and function parameters in serial gated myocardial perfusion SPECT studies. J Nucl Cardiol 2010;17:811–6. [PMID: 20440590] [PMCID: PMC2992839].

Cheung A, Zhou Y, Faber TL, Garcia EV, Zhu L, Chen J. The performance of phase analysis of gated SPECT myocardial perfusion imaging in the presence of perfusion defects: A simulation study. J Nucl Cardiol 2012;19:500–6 [PMID: 22203443] [PMCID: PMC3731539].

Liu et al., “Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review”. JMIR, 22. 2020.

Ferro et al., “Impact of DCM-Causing Genetic Background on Long-Term Response to Cardiac Resynchronization Therapy”. JACC: Clinical Electrophysiology, 10: 1455–1464, 2024.