索引超出了数组界限。 文章摘要
|本期目录/Table of Contents|

[1]周辉,李坤鹏,赵翰文,等.人工智能在心血管病领域的应用和发展[J].国际心血管病杂志,2022,04:216-218.
点击复制

人工智能在心血管病领域的应用和发展(PDF)

《国际心血管病杂志》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2022年04期
页码:
216-218
栏目:
综述
出版日期:
2022-08-30

文章信息/Info

Title:
-
作者:
周辉李坤鹏赵翰文龚敏
232000 淮南市新华医疗集团北方医院( 周辉); 232000 淮南市新华医疗集团新华医院(李坤鹏,赵翰文,龚敏)
Author(s):
-
关键词:
人工智能心血管疾病临床诊断
Keywords:
-
分类号:
-
DOI:
10.3969/j.issn.1673-6583.2022.04.007
文献标识码:
-
摘要:
人工智能是研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术及 应用系统的一门新兴技术。使用人工智能提高心血管疾病的诊疗、管理、预防水平将为心 血管病领域提供新的思路与挑战。该文介绍人工智能对心血管病的诊断、管理、临床决策 方面的机遇和挑战。
Abstract:
-

参考文献/References

[1] Hamet P, Tremblay J. Artificial intelligence in medicine[J]. Metabolism, 2017, 69S:S36-S40.
[2] Mintz Y, Brodie R. Introduction to artificial intelligence in medicine[J]. Minim Invasive Ther Allied Technol, 2019, 28(2):73-81.
[3] Howard J. Artificial intelligence: implications for the future of work[J]. Am J Ind Med, 2019. 62(11):917-926.
[4] Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future[J]. Stroke Vasc Neurol, 2017, 2(4):230-243.
[5] 张远望. 人工智能与应用[J]. 中国科技纵横, 2015, 20:22.
[6] 黄刚, 余秀琼, 刘汉雄, 等. 心血管病领域人工智能的应用及 展望[J]. 中华医学杂志, 2020, 100(45):3649-52.
[7] Wang S, Zhang S, Li Z, et al. Automatic digital ECG signal extraction and normal QRS recognition from real scene ECG images[J]. Comput Methods Programs Biomed, 2020, 187:105254.
[8] Costa CM, Silva IS, de Sousa RD, et al. The association between reconstructed phase space and artificial neural networks for vectorcardiographic recognition of myocardial infarction[J]. J Electrocardiol, 2018, 51(3):443-449.
[9] Han C, Shi L. ML-ResNet: anovel network to detect and locate myocardial infarction using 12 leads ECG[J]. Comput Methods Programs Biomed, 2020, 185:105138.
[10] Attia ZI, Kapa S, Lopez-Jimenez F, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram[J]. Nat Med, 2019, 25(1):70-74.
[11] Zamorano JL, Pinto FJ, Solano-López J, et al. The year in cardiovascular medicine 2020: imaging[J]. Eur Heart J, 2021, 42(7):740-749.
[12] Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms[J]. NPJ Digit Med, 2020, 3:10.
[13] Tamborini G, Piazzese C, Lang RM, et al. Feasibility and accuracy of automated software for transthoracic threedimensional left ventricular volume and function analysis: comparisons with two-dimensional echocardiography, threedimensional transthoracic manual method, and cardiac magnetic resonance imaging[J]. J Am Soc Echocardiogr, 2017, 30(11):1049-1058.
[14] Han D, Lee JH, Rizvi A, et al. Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: a machine learning approach[J]. J Nucl Cardiol, 2018, 25(1):223-233.
[15] Dey D, Gaur S, Ovrehus KA, et al. Integrated prediction of lesion-specific ischaemia from quantitative coronary CT angiography using machine learning: a multicentre study[J]. Eur Radiol, 2018, 28(6):2655-2664.
[16] Busse A, Rajagopal R, Yücel S, et al. Cardiac MRI-update 2020[J]. Radiologe, 2020, 60(Suppl 1):33-40.
[17] Kim PK, Hong YJ, Im DJ, et al. Myocardial T1 and T2 mapping: techniques and clinical applications[J]. Korean J Radiol, 2017, 18(1):113-131.
[18] Sharma A, Okada DR, Yacoub H, et al. Diagnosis of cardiac sarcoidosis: an era of paradigm shift[J]. Ann Nucl Med, 2020, 34(2):87-93.
[19] Brown LAE, Onciul SC, Broadbent DA, et al. Fully automated, inline quantification of myocardial blood flow with cardiovascular magnetic resonance: repeatability of measurements in healthy subjects[J]. J Cardiovasc Magn Reson, 2018, 20(1):48.
[20] Cho H, Lee JG, Kang SJ, et al. Angiography-based machine learning for predicting fractional flow reserve in intermediate coronary artery lesions[J]. J Am Heart Assoc, 2019, 8(4):e011685.
[21] Jun TJ, Kang SJ, Lee JG, et al. Automated detection of vulnerable plaque in intravascular ultrasound images[J]. Med Biol Eng Comput, 2019, 57(4):863-876.
[22] Nam HS, Kim CS, Lee JJ, et al. Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage[J]. Med Phys, 2016, 43(4):1662.
[23] From the American Association of Neurological Surgeons (AANS), American Society of Neuroradiology (ASNR), Cardiovascular and Interventional Radiology Society of Europe (CIRSE), et al. Multisociety consensus quality improvement revised consensus statement for endovascular therapy of acute ischemic stroke[J]. Int J Stroke, 2018, 13(6):612-632.
[24] Swaminathan RV, Rao SV. Robotic-assisted transradial diagnostic coronary angiography[J]. Catheter Cardiovasc Interv, 2018 , 92(1):54-57.
[25] Lo N, Gutierrez JA, Swaminathan RV. Robotic-assisted percutaneous coronary intervention[J]. Curr Treat Options Cardiovasc Med, 2018, 20(2):14.
[26] Cho IJ, Sung JM, Kim HC, et al. Development and external validation of a deep learning algorithm for prognostication of cardiovascular outcomes[J]. Korean Circ J, 2020, 50(1):72-84.
[27] Sung JM, Cho IJ, Sung D, et al. Development and verification of prediction models for preventing cardiovascular diseases[J]. PLoS One, 2019, 14(9):e0222809.
[28] Motwani M, Dey D, Berman DS, et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis[J]. Eur Heart J, 2017, 38(7):500-507.
[29] Lacson RC, Baker B, Suresh H, et al. Use of machinelearning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients[J]. Clin Kidney J, 2018, 12(2):206-212.
[30] Harish V, Morgado F, Stern AD, et al. Artificial intelligence and clinical decision making: the new nature of medical uncertainty[J]. Acad Med, 2021, 96(1):31-36.
[31] Strianese O, Rizzo F, Ciccarelli M, et al. Precision and personalized medicine: how genomic approach improves the management of cardiovascular and neurodegenerative disease[J]. Genes (Basel), 2020, 11(7):747.
[32] Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine[J]. N Engl J Med, 2016, 375(13):1216-1219.
[33] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436-44.
[34] Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare[J]. Nat Biomed Eng, 2018, 2(10):719-731.

备注/Memo

备注/Memo:
通信作者:龚敏, Email: 9984645@qq.com
更新日期/Last Update: 2022-08-30