ABSTRACT
Nuclear cardiology methods play a pivotal role in the diagnostic and prognostic assessment of coronary artery disease. Recently, the integration of artificial intelligence algorithms has emerged as a strategy to enhance the functional and diagnostic efficacy of these established methods. Artificial intelligence encompasses a spectrum of computational, classification, and analytical techniques designed to emulate human intelligence. Its application has yielded notable advancements in clinical processes related to diseases, particularly through the prompt and precise interpretation of medical images. In the realm of nuclear cardiology, artificial intelligence has progressively assumed a substantive role across all facets of imaging, spanning from data acquisition to interpretation.
Keywords:
Artificial intelligent, nuclear cardiology, analysis
References
1
Russell S, Norvig P. Artificial intelligence: a modern approach. 3rd edition. Pearson Education Limited; 2016.
2
Géron A. Hands-on machine learnng with scikit-learn, Keras, and tensorflow: concepts, tools, and techniques to build intelligent systems. 2nd edition. Incorperated, editor. O’Reilly Media, USA; 2019.
3
Li J, Yang G, Zhang L. Artificial Intelligence Empowered Nuclear Medicine and Molecular Imaging in Cardiology: A State-of-the-Art Review. Phenomics 2023;3:586-596.
4
Visvikis D, Cheze Le Rest C, Jaouen V, Hatt M. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging 2019;46:2630-2637.
5
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to Radiomics. J Nucl Med 2020;61:488-495.
6
Ramon AJ, Yang Y, Pretorius PH, et al. Initial investigation of low-dose SPECT-MPI via deep learning. IEEE Nucl Sci Symp 2018;1-3.
7
Song C, Yang Y, Wernick MN, et al. Low-dose cardiac-gated spect studies using a residual convolutional neural network. IEEE Int Symp Biomed Imaging 2019;1:653-656.
8
Wang B, Liu H. FBP-Net for direct reconstruction of dynamic PET images. Phys Med Biol 2020;6.
9
Hu Z, Xue H, Zhang Q, et al. DPIR-Net: Direct PET image reconstruction based on the Wasserstein generative adversarial network. IEEE Trans Radiat Plasma Med Sci 2020.
10
Lee JS. A review of deep learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci 2021;5.
11
Li T, Zhang M, Qi W, Asma E, Qi J. Motion correction of respiratory-gated PET images using deep learning based image registration framework. Phys Med Biol 2020;65:155003.
12
Shi L, Lu Y, Dvornek N, et al. Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning. IEEE Trans Med Imaging 2021;40:3293-3304.
13
Hagio T, Poitrasson-Rivière A, Moody JB, et al. “Virtual” attenuation correction: improving stress myocardial perfusion SPECT imaging using deep learning. Eur J Nucl Med Mol Imaging 2022;49:3140-3149.
14
Yang J, Shi L, Wang R, et al. Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study. J Nucl Med 2021;62:1645-1652.
15
Liu F, Jang H, Kijowski R, Bradshaw T, McMillan AB. Deep Learning MR Imaging-based Attenuation Correction for PET/MR Imaging. Radiology 2018;286:676-684.
16
Tao Q, Yan W, Wang Y, et al. Deep Learning-based Method for Fully Automatic Quantification of Left Ventricle Function from Cine MR Images: A Multivendor, Multicenter Study. Radiology 2019;290:81-88.
17
Guo Z, Li X, Huang H, Guo N, Li Q. Deep Learning-based Image Segmentation on Multimodal Medical Imaging. IEEE Trans Radiat Plasma Med Sci 2019;3:162-169.
18
Zeleznik R, Foldyna B, Eslami P, et al. Deep convolutional neural networks to predict cardiovascular risk from computed tomography. Nat Commun 2021;12:715.
19
Kwiecinski J, Tzolos E, Meah MN, et al. Machine Learning with 18F-Sodium Fluoride PET and Quantitative Plaque Analysis on CT Angiography for the Future Risk of Myocardial Infarction. J Nucl Med 2022;63:158-165.
20
Betancur J, Hu LH, 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 2019;60:664-670.
21
Eisenberg E, Miller RJH, Hu LH, et al. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. J Nucl Cardiol 2022;29:2295-2307.
22
Otaki Y, Singh A, Kavanagh P, et al. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease. JACC Cardiovasc Imaging 2022;15:1091-1102.
23
Hu LH, Miller RJH, 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 2021;22:705-714.
24
Juarez-Orozco LE, Martinez-Manzanera O, van der Zant FM, Knol RJJ, Knuuti J. Deep Learning in Quantitative PET Myocardial Perfusion Imaging: A Study on Cardiovascular Event Prediction. JACC Cardiovasc Imaging 2020;13:180-182.
25
Rios R, Miller RJH, Hu LH, et al. Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovasc Res 2022;118:2152-2164.
26
Kolossváry M, Kellermayer M, Merkely B, Maurovich-Horvat P. Cardiac Computed Tomography Radiomics: A Comprehensive Review on Radiomic Techniques. J Thorac Imaging 2018;33:26-34.
27
Motwani M. Hiding beyond plain sight: Textural analysis of positron emission tomography to identify high-risk plaques in carotid atherosclerosis. J Nucl Cardiol 2021;28:1872-1874.
28
Tawakol A, Migrino RQ, Bashian GG, et al. In vivo 18F-fluorodeoxyglucose positron emission tomography imaging provides a noninvasive measure of carotid plaque inflammation in patients. J Am Coll Cardiol 2006;48:1818-1824.
29
Otaki Y, Miller RJH, Slomka PJ. The application of artificial intelligence in nuclear cardiology. Ann Nucl Med 2022;36:111-122.
30
Geis JR, Brady AP, Wu CC, et al. Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement. J Am Coll Radiol 2019;16:1516-1521.
