Artificial Intelligence in Nuclear Cardiology– Review of Current Status and Recent Advancements

Salerno M, Beller GA. Noninvasive assessment of myocardial perfusion. Circ Cardiovasc Imaging. 2009;2(5):412–24.

Article  PubMed  Google Scholar 

Gomez J, Doukky R. Artificial intelligence in nuclear cardiology. J Nucl Med. 2019;60(8):1042–3.

Article  PubMed  Google Scholar 

Lekadir K, Leiner T, Young AA, Petersen SE. Editorial: current and future role of artificial intelligence in cardiac imaging. Front Cardiovasc Med. 2020;7:137.

Article  PubMed  PubMed Central  Google Scholar 

Slomka PJ, Miller RJ, Isgum I, Dey D. Application and translation of artificial intelligence to cardiovascular imaging in nuclear medicine and Noncontrast CT. Semin Nucl Med. 2020;50(4):357–66.

Article  PubMed  Google Scholar 

Motwani M. 2022 Artificial intelligence primer for the nuclear cardiologist. J Nucl Cardiol. 2023;30(6):2441-53.

Koulaouzidis G, Jadczyk T, Iakovidis DK, Koulaouzidis A, Bisnaire M, Charisopoulou D. Artificial intelligence in Cardiology-A narrative review of current status. J Clin Med. 2022;11:13.

Article  Google Scholar 

Slart R, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans A, et al. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol Imaging. 2021;48(5):1399–413.

Article  PubMed  PubMed Central  Google Scholar 

Slomka PJ, Betancur J, Liang JX, Otaki Y, Hu LH, Sharir T, et al. Rationale and design of the registry of fast myocardial perfusion imaging with next generation SPECT (REFINE SPECT). J Nucl Cardiol. 2020;27(3):1010–21.

Article  PubMed  Google Scholar 

Krajcer Z. Artificial intelligence in cardiovascular medicine: historical overview, current status, and future directions. Tex Heart Inst J. 2022;49(2).

Case JA. Deep learning and artificial intelligence: what does the cardiologist really need to know?? Circ Cardiovasc Imaging. 2022;15(9):e014744.

Article  PubMed  Google Scholar 

Krittanawong C, Johnson KW, Rosenson RS, Wang Z, Aydar M, Baber U, et al. Deep learning for cardiovascular medicine: a practical primer. Eur Heart J. 2019;40(25):2058–73.

Article  PubMed  PubMed Central  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.

Article  PubMed  PubMed Central  Google Scholar 

Seetharam K, Min JK. Artificial intelligence and machine learning in cardiovascular imaging. Methodist Debakey Cardiovasc J. 2020;16(4):263–71.

Article  PubMed  PubMed Central  Google Scholar 

Kufel J, Bargiel-Laczek K, Kocot S, Kozlik M, Bartnikowska W, Janik M, et al. What is machine learning, artificial neural networks and deep Learning?-Examples of practical applications in medicine. Diagnostics (Basel). 2023;13:15.

Google Scholar 

Litjens G, Ciompi F, Wolterink JM, de Vos BD, Leiner T, Teuwen J, et al. State-of-the-Art deep learning in cardiovascular image analysis. JACC Cardiovasc Imaging. 2019;12(8 Pt 1):1549–65.

Article  PubMed  Google Scholar 

Miller RJH. Artificial intelligence in nuclear cardiology. Cardiol Clin. 2023;41(2):151–61.

Article  PubMed  Google Scholar 

Muscogiuri G, Volpato V, Cau R, Chiesa M, Saba L, Guglielmo M, et al. Application of AI in cardiovascular multimodality imaging. Heliyon. 2022;8(10):e10872.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Salih A, Boscolo Galazzo I, Gkontra P, Lee AM, Lekadir K, Raisi-Estabragh Z, et al. Explainable artificial intelligence and cardiac imaging: toward more interpretable models. Circ Cardiovasc Imaging. 2023;16(4):e014519.

Article  PubMed  Google Scholar 

Garcia EV, Piccinelli M. Preparing for the artificial intelligence revolution in nuclear cardiology. Nucl Med Mol Imaging. 2023;57(2):51–60.

Article  PubMed  Google Scholar 

Otaki Y, Singh A, Kavanagh P, Miller RJH, Parekh T, Tamarappoo BK, et al. Clinical deployment of explainable artificial intelligence of SPECT for diagnosis of coronary artery disease. JACC Cardiovasc Imaging. 2022;15(6):1091–102.

Article  PubMed  Google Scholar 

Miller RJH, Kuronuma K, Singh A, Otaki Y, Hayes S, Chareonthaitawee P, et al. Explainable deep learning improves physician interpretation of myocardial perfusion imaging. J Nucl Med. 2022;63(11):1768–74.

PubMed  PubMed Central  Google Scholar 

Diamond GA, Forrester JS. Analysis of probability as an aid in the clinical diagnosis of coronary-artery disease. N Engl J Med. 1979;300(24):1350–8.

Article  CAS  PubMed  Google Scholar 

Pryor DB, Shaw L, McCants CB, Lee KL, Mark DB, Harrell FE Jr., et al. Value of the history and physical in identifying patients at increased risk for coronary artery disease. Ann Intern Med. 1993;118(2):81–90.

Article  CAS  PubMed  Google Scholar 

Genders TS, Steyerberg EW, Hunink MG, Nieman K, Galema TW, Mollet NR, et al. Prediction model to estimate presence of coronary artery disease: retrospective pooled analysis of existing cohorts. BMJ. 2012;344:e3485.

Article  PubMed  PubMed Central  Google Scholar 

Miller RJH, Hauser MT, Sharir T, Einstein AJ, Fish MB, Ruddy TD, et al. Machine learning to predict abnormal myocardial perfusion from pre-test features. J Nucl Cardiol. 2022;29(5):2393–403.

Article  PubMed  PubMed Central  Google Scholar 

Eisenberg E, Miller RJH, Hu LH, Rios R, Betancur J, Azadani P, et al. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. J Nucl Cardiol. 2022;29(5):2295–307.

Article  PubMed  Google Scholar 

Shiri I, AmirMozafari Sabet K, Arabi H, Pourkeshavarz M, Teimourian B, Ay MR, et al. Standard SPECT myocardial perfusion Estimation from half-time acquisitions using deep convolutional residual neural networks. J Nucl Cardiol. 2021;28(6):2761–79.

Article  PubMed  Google Scholar 

Lecchi M, Martinelli I, Zoccarato O, Maioli C, Lucignani G, Del Sole A. Comparative analysis of full-time, half-time, and quarter-time myocardial ECG-gated SPECT quantification in normal-weight and overweight patients. J Nucl Cardiol. 2017;24(3):876–87.

Article  CAS  PubMed  Google Scholar 

Zhu F, Li L, Zhao J, Zhao C, Tang S, Nan J, et al. A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images. Comput Biol Med. 2023;160:106954.

Article  PubMed  Google Scholar 

Wang T, Lei Y, Tang H, He Z, Castillo R, Wang C, et al. A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study. J Nucl Cardiol. 2020;27(3):976–87.

Article  PubMed  Google Scholar 

Shi L, Onofrey JA, Liu H, Liu YH, Liu C. Deep learning-based Attenuation map generation for myocardial perfusion SPECT. Eur J Nucl Med Mol Imaging. 2020;47(10):2383–95.

Article  PubMed  Google Scholar 

Shi L, Lu Y, Dvornek N, Weyman CA, Miller EJ, Sinusas AJ, et al. Automatic Inter-Frame patient motion correction for dynamic cardiac PET using deep learning. IEEE Trans Med Imaging. 2021;40(12):3293–304.

Article  PubMed  PubMed Central  Google Scholar 

Rahman A, Zhu Y, Clarkson E, Kupinski MA, Frey EC, Jha AK. Fisher information analysis of list-mode SPECT emission data for joint Estimation of activity and Attenuation distribution. Inverse Probl. 2020;36(8).

Chen X, Zhou B, Xie H, Shi L, Liu H, Holler W, et al. Direct and indirect strategies of deep-learning-based Attenuation correction for general purpose and dedicated cardiac SPECT. Eur J Nucl Med Mol Imaging. 2022;49(9):3046–60.

Article  PubMed  PubMed Central  Google Scholar 

Hagio T, Poitrasson-Riviere A, Moody JB, Renaud JM, Arida-Moody L, Shah RV, et al. Virtual Attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning. Eur J Nucl Med Mol Imaging. 2022;49(9):3140–9.

Article  PubMed  Google Scholar 

Shanbhag AD, Miller RJH, Pieszko K, Lemley M, Kavanagh P, Feher A, et al. Deep Learning-Based Attenuation correction improves diagnostic accuracy of cardiac SPECT. J Nucl Med. 2023;64(3):472–8.

Article  CAS 

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