Richardson WJ, Clarke SA, Quinn TA, Holmes JW. Physiological implications of myocardial scar structure. Compr Physiol. 2015;5(4):1877–909. https://doi.org/10.1002/cphy.c140067.

Article  PubMed  PubMed Central  Google Scholar 

Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, et al. Heart disease and stroke statistics-2017 update: a report from the American Heart Association. Circulation. 2017;135(10):e146–603. https://doi.org/10.1161/CIR.0000000000000485.

Article  PubMed  PubMed Central  Google Scholar 

Jones NR, Roalfe AK, Adoki I, Hobbs FDR, Taylor CJ. Survival of patients with chronic heart failure in the community: a systematic review and meta-analysis. Eur J Heart Fail. 2019;21(11):1306–25. https://doi.org/10.1002/ejhf.1594.

Article  PubMed  Google Scholar 

Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, et al. Global, regional, and national burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1–25. https://doi.org/10.1016/j.jacc.2017.04.052.

Article  PubMed  PubMed Central  Google Scholar 

Wollert KC, Meyer GP, Lotz J, Ringes-Lichtenberg S, Lippolt P, Breidenbach C, et al. Intracoronary autologous bone-marrow cell transfer after myocardial infarction: the BOOST randomised controlled clinical trial. Lancet. 2004;364(9429):141–8. https://doi.org/10.1016/S0140-6736(04)16626-9.

Article  PubMed  Google Scholar 

Traverse JH, Henry TD, Vaughan DE, Ellis SG, Pepine CJ, Willerson JT, et al. LateTIME: a phase-II, randomized, double-blinded, placebo-controlled, pilot trial evaluating the safety and effect of administration of bone marrow mononuclear cells 2 to 3 weeks after acute myocardial infarction. Tex Heart Inst J. 2010;37(4):412–20.

PubMed  PubMed Central  Google Scholar 

Traverse JH, Henry TD, Pepine CJ, Willerson JT, Chugh A, Yang PC, et al. TIME trial: effect of timing of stem cell delivery following ST-elevation myocardial infarction on the recovery of global and regional left ventricular function: final 2-year analysis. Circ Res. 2018;122(3):479–88. https://doi.org/10.1161/CIRCRESAHA.117.311466.

CAS  Article  PubMed  Google Scholar 

Henry TD, Pepine CJ, Lambert CR, Traverse JH, Schatz R, Costa M, et al. The Athena trials: autologous adipose-derived regenerative cells for refractory chronic myocardial ischemia with left ventricular dysfunction. Catheter Cardiovasc Interv. 2017;89(2):169–77. https://doi.org/10.1002/ccd.26601.

Article  PubMed  Google Scholar 

Surder D, Manka R, Lo Cicero V, Moccetti T, Rufibach K, Soncin S, et al. Intracoronary injection of bone marrow-derived mononuclear cells early or late after acute myocardial infarction: effects on global left ventricular function. Circulation. 2013;127(19):1968–79. https://doi.org/10.1161/CIRCULATIONAHA.112.001035.

Article  PubMed  Google Scholar 

Wollert KC, Meyer GP, Muller-Ehmsen J, Tschope C, Bonarjee V, Larsen AI, et al. Intracoronary autologous bone marrow cell transfer after myocardial infarction: the BOOST-2 randomised placebo-controlled clinical trial. Eur Heart J. 2017;38(39):2936–43. https://doi.org/10.1093/eurheartj/ehx188.

CAS  Article  PubMed  Google Scholar 

Musunuru K, Sheikh F, Gupta RM, Houser SR, Maher KO, Milan DJ, et al. Induced pluripotent stem cells for cardiovascular disease modeling and precision medicine: a scientific statement from the American Heart Association. Circulation: Genomic and Precision Medicine. 2018;11(1). https://doi.org/10.1161/hcg.0000000000000043.

Augustine R, Dan P, Hasan A, Khalaf IM, Prasad P, Ghosal K, et al. Stem cell-based approaches in cardiac tissue engineering: controlling the microenvironment for autologous cells. Biomed Pharmacother. 2021;138: 111425. https://doi.org/10.1016/j.biopha.2021.111425.

CAS  Article  PubMed  Google Scholar 

Klein SG, Alsolami SM, Steckbauer A, Arossa S, Parry AJ, Ramos Mandujano G, et al. A prevalent neglect of environmental control in mammalian cell culture calls for best practices. Nat Biomed Eng. 2021;5(8):787–92. https://doi.org/10.1038/s41551-021-00775-0.

Article  PubMed  Google Scholar 

Refresh cell culture. Nat Biomed Eng. 2021;5(8):783–4. https://doi.org/10.1038/s41551-021-00790-1.

Article  Google Scholar 

Banerjee MN, Bolli R, Hare JM. Clinical studies of cell therapy in cardiovascular medicine: recent developments and future directions. Circ Res. 2018;123(2):266–87. https://doi.org/10.1161/CIRCRESAHA.118.311217.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Lau JF, Anderson SA, Adler E, Frank JA. Imaging approaches for the study of cell-based cardiac therapies. Nat Rev Cardiol. 2010;7(2):97–105. https://doi.org/10.1038/nrcardio.2009.227.

Article  PubMed  Google Scholar 

Dey D, Slomka PJ, Leeson P, Comaniciu D, Shrestha S, Sengupta PP, et al. Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. J Am Coll Cardiol. 2019;73(11):1317–35. https://doi.org/10.1016/j.jacc.2018.12.054.

Article  PubMed  PubMed Central  Google Scholar 

Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–58. https://doi.org/10.1056/NEJMra1814259.

Article  PubMed  Google Scholar 

Seetharam K, Shresthra S, Mills JD, Sengupta PP. Artificial intelligence in nuclear cardiology: adding value to prognostication. Current Cardiovascular Imaging Reports. 2019;12(5). https://doi.org/10.1007/s12410-019-9490-8.

Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP. Machine learning in cardiovascular medicine: are we there yet? Heart. 2018;104(14):1156–64. https://doi.org/10.1136/heartjnl-2017-311198.

Martin-Isla C, Campello VM, Izquierdo C, Raisi-Estabragh Z, Baessler B, Petersen SE, et al. Image-based cardiac diagnosis with machine learning: a review. Front Cardiovasc Med. 2020;7:1. https://doi.org/10.3389/fcvm.2020.00001.

Article  PubMed  PubMed Central  Google Scholar 

Cetin I, Raisi-Estabragh Z, Petersen SE, Napel S, Piechnik SK, Neubauer S, et al. Radiomics signatures of cardiovascular risk factors in cardiac MRI: results from the UK Biobank. Front Cardiovasc Med. 2020;7: 591368. https://doi.org/10.3389/fcvm.2020.591368.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Henglin M, Stein G, Hushcha PV, Snoek J, Wiltschko AB, Cheng S. Machine learning approaches in cardiovascular imaging. Circ Cardiovasc Imaging. 2017;10(10). https://doi.org/10.1161/CIRCIMAGING.117.005614.

Leiner T, Rueckert D, Suinesiaputra A, Baessler B, Nezafat R, Isgum I, et al. 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 

Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920–30. https://doi.org/10.1161/CIRCULATIONAHA.115.001593.

Article  PubMed  PubMed Central  Google Scholar 

Tomaszewski JE, Hipp J, Tangrea M, Madabhushi A. Machine vision and machine learning in digital pathology. In: Linda MM, Richard NM, editors. Pathobiology of human disease. San Diego: Academic Press; 2014. p. 3711–22.

Chapter  Google Scholar 

LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. https://doi.org/10.1038/nature14539.

CAS  Article  PubMed  Google Scholar 

El-Amir H, Hamdy M. A gentle introduction. Apress; 2020:p. 3–36.

Kagiyama N, Shrestha S, Farjo PD, Sengupta PP. Artificial intelligence: practical primer for clinical research in cardiovascular disease. J Am Heart Assoc. 2019;8(17):e012788. https://doi.org/10.1161/JAHA.119.012788.

Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, et al. Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med. 2020;7:25. https://doi.org/10.3389/fcvm.2020.00025.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. CoRR. 2015;abs/1412.6572.

Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. IEEE. 2015.

Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(4):640–51. https://doi.org/10.1109/TPAMI.2016.2572683.

Article  PubMed  Google Scholar 

Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. Springer International Publishing; 2015. p. 234–41.

Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. UNet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging. 2020;39(6):1856–67. https://doi.org/10.1109/TMI.2019.2959609.

Article  PubMed  Google Scholar 

• Siddique N, Paheding S, Elkin CP, Devabhaktuni V. U-Net and its variants for medical image segmentation: a review of theory and applications. IEEE Access. 2021;9:82031–57. https://doi.org/10.1109/ACCESS.2021.3086020. Findings from this review summarize the basics and contemporary use of U-Net neural networks for biomedical image segmentation.

Article  Google Scholar 

Poetsch MS, Strano A, Guan K. Human–induced pluripotent stem cells: from cell origin, genomic stability, and epigenetic memory to translational medicine. Stem Cells. 2022. https://doi.org/10.1093/stmcls/sxac020.

Article  PubMed  PubMed Central  Google Scholar 

Streckfuss-Bömeke K, Wolf F, Azizian A, Stauske M, Tiburcy M, Wagner S, et al. Comparative study of human-induced pluripotent stem cells derived from bone marrow cells, hair keratinocytes, and skin fibroblasts. Eur Heart J. 2012;34(33):2618–29. https://doi.org/10.1093/eurheartj/ehs203.

CAS  Article  PubMed  Google Scholar 

Hu S, Zhao M-T, Jahanbani F, Shao N-Y, Lee WH, Chen H, et al. Effects of cellular origin on differentiation of human induced pluripotent stem cell–derived endothelial cells. JCI Insight. 2016;1(8). https://doi.org/10.1172/jci.insight.85558.

Raab S, Klingenstein M, Liebau S, Linta L. A comparative view on human somatic cell sources for iPSC generation. Stem Cells International. 2014;2014:1–12. https://doi.org/10.1155/2014/768391.

Article  Google Scholar 

McGillicuddy N, Floris P, Albrecht S, Bones J. Examining the sources of variability in cell culture media used for biopharmaceutical production. Biotechnol Lett. 2018;40(1):5–21. https://doi.org/10.1007/s10529-017-2437-8.

CAS  Article  PubMed  Google Scholar 

Koyanagi-Aoi M, Ohnuki M, Takahashi K, Okita K, Noma H, Sawamura Y, et al. Differentiation-defective phenotypes revealed by large-scale analyses of human pluripotent stem cells. Proc Natl Acad Sci U S A. 2013;110(51):20569–74. https://doi.org/10.1073/pnas.1319061110.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Tu C, Chao BS, Wu JC. Strategies for improving the maturity of human induced pluripotent stem cell-derived cardiomyocytes. Circ Res. 2018;123(5):512–4. https://doi.org/10.1161/circresaha.118.313472.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Kannan S, Kwon C. Regulation of cardiomyocyte maturation during critical perinatal window. J Physiol. 2020;598(14):2941–56. https://doi.org/10.1113/jp276754.

CAS  Article  PubMed  Google Scholar 

Williams B, Löbel W, Finklea F, Halloin C, Ritzenhoff K, Manstein F, et al. Prediction of human induced pluripotent stem cell cardiac differentiation outcome by multifactorial process modeling. Frontiers in Bioengineering and Biotechnology. 2020;8. https://doi.org/10.3389/fbioe.2020.00851.

Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell. 2006;126(4):663–76. https://doi.org/10.1016/j.cell.2006.07.024.