Publicação científica trimestral do CREMERJ - volume 2 - número 3 - 2023

79 Med. Ciên. e Arte , Rio de Janeiro, v.2, n.3, p.61-79, jul-set 2023 Cintilografia de Perfusão Miocárdica: aplicações e avanços recentes Claudio Tinoco Mesquita et al. 47. Motwani M, Berman DS, Germano G, Slomka P. Automated Quantitative Nuclear Cardiology Methods. Vol. 34, Cardiology Clinics. W.B. Saunders; 2016. p. 47-57. 48. Otaki Y, Betancur J, Sharir T, Hu LH, Gransar H, Liang JX, et al. 5-Year Prognostic Value of Quantitative Versus Visual MPI in Subtle Perfusion Defects: Results From REFINE SPECT. JACC Cardiovasc Imaging. 2020 Mar 1;13(3):774-85. 49. Hu LH, Miller RJH, Sharir T, Commandeur F, Rios R, Einstein AJ, 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 Jun 1;22(6):705-14. 50. Nensa F, Demircioglu A, Rischpler C. Artificial intelligence in nuclear medicine. Journal of Nuclear Medicine. 2019 Sep 1;60(9):29S-37S. 51. 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. Vol. 73, Journal of the American College of Cardiology. Elsevier USA; 2019. p. 1317-35. 52. Papandrianos NI, Feleki A, Papageorgiou EI, Martini C. Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images. J Clin Med. 2022 Jul 1;11(13).