谢圆圆 , 李冬梅 , 毛鑫乐 , 邓燕 . 人工智能在超声诊断冠心病中的应用现状[J]. 中华医学超声杂志（电子版）, 2024 , 21(02) : 170 -174 . DOI: 10.3877/cma.j.issn.1672-6448.2024.02.011

1	Writing committee of the report on cardiovascular health and diseases in china. Report on cardiovascular health and diseases in China 2021: an updated summary[J]. Biomed Environ Sci, 2022, 35(7): 573-603.

2	Liu S, Li Y, Zeng X, et al. Burden of cardiovascular diseases in China, 1990-2016: findings from the 2016 global burden of disease study[J]. JAMA Cardiol, 2019, 4(4): 342-352.

3	刘梦怡, 吴伟春. 人工智能在超声心动图中的应用现状及进展[J/OL].中华医学超声杂志(电子版), 2021, 18(2): 216-219.

4	赵娜, 郑权, 张瑞芳. 超声医学质量控制与区域医疗合作信息化建设初探[J/OL].中华医学超声杂志(电子版), 2020, 17(7): 614-618.

5	Tseng AS, Lopez-Jimenez F, Pellikka PA. Future guidelines for artificial intelligence in echocardiography[J]. J Am Soc Echocardiogr, 2022, 35(8): 878-882.

6	Laumer F, Di Vece D, Cammann VL, et al. Assessment of artificial intelligence in echocardiography diagnostics in differentiating Takotsubo syndrome from myocardial infarction[J]. JAMA Cardiol, 2022, 7(5): 494-503.

7	Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023[J]. N Engl J Med, 2023, 388(13): 1201-1208.

8	van der Velden BHM, Kuijf HJ, Gilhuijs KGAet al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J]. Med Image Anal, 2022, 79: 102470.

9	Coulter SA, Campos K. Artificial intelligence in echocardiography[J]. Tex Heart Inst J, 2022, 49(2): e217671.

10	Watson X, D'Souza J, Cooper D, et al. Artificial intelligence in cardiology: fundamentals and applications[J]. Intern Med J, 2022, 52(6): 912-920.

11	Chu WK, Raeside DE. Fourier analysis of the echocardiogram[J]. Phys Med Biol, 1978, 23(1): 100-105.

12	Kazuo K, Kimura T, Ishihara M, et al. JCS 2018 guideline on diagnosis and treatment of acute coronary syndrome[J]. Circ J, 2019, 83: 1085-1196.

13	Leung KY, Bosch JG. Localized shape variations for classifying wall motion in echocardiograms[J]. Med Image Comput Comput Assist Interv, 2007, 10(Pt 1): 52-59.

14	Kusunose K, Abe T, Haga A, et al. A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images[J]. JACC Cardiovasc Imaging, 2020, 13(2 Pt 1): 374-381.

15	Huang MS, Wang CS, Chiang JH, et al. Automated recognition of regional wall motion abnormalities through deep neural network interpretation of transthoracic echocardiography[J]. Circulation, 2020, 142(16): 1510-1520.

16	Muraki R, Teramoto A, Sugimoto K, et al. Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory[J]. PLoS One, 2022, 17(2): e0264002.

17	Lin X, Yang F, Chen Y, et al. Echocardiography-based AI detection of regional wall motion abnormalities and quantification of cardiac function in myocardial infarction[J]. Front Cardiovasc Med, 2022, 9: 903660.

18	王伟. 超声心动图在冠心病患者中的诊断效果[J]. 当代医学, 2022, 28(20): 77-79.

19	Federico M, Poilvert N, Abraham T, et al. Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert[J]. Circ Cardiovasc Imaging, 2019, 12(9): e009303.

20	Moal O, Roger E, Lamouroux A, et al. Explicit and automatic ejection fraction assessment on 2D cardiac ultrasound with a deep learning-based approach[J]. Comput Biol Med, 2022, 146: 105637.

21	Bombelli M, Vanoli J, Facchetti R, et al. Impact of the increase in left ventricular mass on the risk of long-term cardiovascular mortality: a prospective cohort study[J]. Hypertension, 2023, 80(6): 1321-1330.

22	Streiff C, Zhu M, Panosian J, et al. Comprehensive evaluation of cardiac function and detection of myocardial infarction based on a semi-automated analysis using full-volume real time three-dimensional echocardiography[J]. Echocardiography, 2015, 32(2): 332-338.

23	Volpato V, Mor-Avi V, Narang A, et al. Automated, machine learning-based, 3D echocardiographic quantification of left ventricular mass[J]. Echocardiography, 2019, 36(2): 312-319.

24	Woodward W, Dockerill C, McCourt A, et al. Real-world performance and accuracy of stress echocardiography: the EVAREST observational multi-centre study[J]. Eur Heart J Cardiovasc Imaging, 2022, 23(5): 689-698.

25	Upton R, Mumith A, Beqiri A, et al. Automated echocardiographic detection of severe coronary artery disease using artificial intelligence[J]. JACC Cardiovasc Imaging, 2022, 15(5): 715-727.

26	O'Driscoll JM, Hawkes W, Beqiri A, et al. Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography[J]. Eur Heart J Open, 2022, 2(5): oeac059.

27	Li Y, Chahal N, Senior R, et al. Reproducible computer-assisted quantification of myocardial perfusion with contrast-enhanced ultrasound[J]. Ultrasound Med Biol, 2017, 43(10): 2235-2246.

28	Li M, Zeng D, Xie Q, et al. A deep learning approach with temporal consistency for automatic myocardial segmentation of quantitative myocardial contrast echocardiography[J]. Int J Cardiovasc Imaging, 2021, 37(6): 1967-1978.

29	Mendizabal-Ruiz EG, Rivera M, Kakadiaris IA. Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach[J]. Med Image Anal, 2013, 17(6): 649-670.

30	Nishi T, Yamashita R, Imura S, et al. Deep learning-based intravascular ultrasound segmentation for the assessment of coronary artery disease[J]. Int J Cardiol, 2021, 333: 55-59.

31	Sheet D, Karamalis A, Eslami A, et al. Joint learning of ultrasonic backscattering statistical physics and signal confidence primal for characterizing atherosclerotic plaques using intravascular ultrasound[J]. Med Image Anal, 2014, 18(1): 103-117.

32	Kang SJ, Ahn JM, Han S, et al. Multimodality imaging of attenuated plaque using grayscale and virtual histology intravascular ultrasound and optical coherent tomography[J]. Catheter Cardiovasc Interv, 2016, 88(1): E1-E11.

33	Otsuka F, Sakakura K, Yahagi K, et al. Has our understanding of calcification in human coronary atherosclerosis progressed?[J]. Arterioscler Thromb Vasc Biol, 2014, 34(4): 724-736.

34	Cho H, Kang SJ, Min HS, et al. Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease[J]. Atherosclerosis, 2021, 324: 69-75.