切换至 "中华医学电子期刊资源库"

中华医学超声杂志(电子版) ›› 2023, Vol. 20 ›› Issue (12) : 1308 -1311. doi: 10.3877/cma.j.issn.1672-6448.2023.12.016

综述

超声心动图的人工智能时代
赵嘉欣1, 穆玉明1,()   
  1. 1. 831100 乌鲁木齐,新疆医科大学第一附属医院心脏超声诊断科
  • 收稿日期:2023-02-21 出版日期:2023-12-01
  • 通信作者: 穆玉明

Echocardiography in the era of artificial intelligence

Jiaxin Zhao, Yuming Mu()   

  • Received:2023-02-21 Published:2023-12-01
  • Corresponding author: Yuming Mu
引用本文:

赵嘉欣, 穆玉明. 超声心动图的人工智能时代[J]. 中华医学超声杂志(电子版), 2023, 20(12): 1308-1311.

Jiaxin Zhao, Yuming Mu. Echocardiography in the era of artificial intelligence[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(12): 1308-1311.

人工智能(artificial intelligence,AI)概念自1950年提出至今,在各领域的进展迅速,极大地改变着人们的生活。随着在心血管领域的价值不断被挖掘,AI从成像解释到临床决策得到不同程度的应用。目前,多种成像方式中心脏超声成像因具有便携、实时、成本低等诸多优点而得到临床广泛认可,成为心脏疾病诊疗过程中不可替代的工具。然而,由于心脏所处的解剖位置特殊、检查过程中节律性跳动以及操作人员专业知识水平不同,心脏超声在成像质量和测量重复性方面受到限制。近年来,AI与心脏超声的融合发展在数据处理、图像识别、超声心动图诊断及预后评价等方面表现出明显优势。本文就AI的概念以及其在心脏超声领域中的应用进展进行综述,并对其目前发展的局限性进行分析。

1
Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine [J]. Gastrointest Endosc, 2020, 92(4): 807-812.
2
Zhou J, Du M, Chang S, et al. Artificial intelligence in echocardiography: detection, functional evaluation, and disease diagnosis [J]. Cardiovasc Ultrasound, 2021, 19(1): 1-11.
3
Deshmukh R, Rathi P. Artificial intelligence in medicine [J]. J Assoc Physicians India, 2022, 70(3): 11-12.
4
Koulaouzidis G, Jadczyk T, Iakovidis DK, et al. Artificial intelligence in cardiology-A narrative review of current status [J]. J Clin Med, 2022, 11(13): 3910-3913.
5
Suzuki K. Machine learning in medical imaging before and after introduction of deep learning [J]. J Med Imag Health In, 2017, 34(2): 14-24.
6
Narang A, Bae R, Hong H, et al. Acquisition of diagnostic echocardioimagedata images by novices using a deep learning based image guidance algorithm [J]. JAMA Cardiol, 2021, 6(6): 624-632.
7
Madani A, Arnaout R, Mofrad M, et al. Fast and accurate view classification of echocardiograms using deep learning [J]. NPJ Digit Med, 2018, 1(1): 1-8.
8
Ostvik A, Smistad E, Aase SA, et al. Real-time standard view classification in transthoracic echocardiography using convolutional neural networks [J]. Ultrasound Med Biol, 2019, 45(2): 374-384.
9
Al Kindi D, Househ M, Alam T, et al. Artificial intelligence models for heart chambers segmentation from 2D echocardioimagedata images: a scoping review [J]. Stud Health Technol Inform, 2022, 289: 264-267.
10
Arafati A, Morisawa D, Avendi MR, et al. Generalizable fully automated multi-label segmentation of four-chamber view echocardiograms based on deep convolutional adversarial networks [J]. J R Soc Interface, 2020, 17(169): 20200267.
11
Painchaud N, Duchateau N, Bernard O, et al. Echocardiography segmentation with enforced temporal consistency [J]. IEEE Trans Med Imaging, 2022, 41(10): 2867-2878.
12
Gandhi S, Mosleh W, Shen J, et al. Automation, machine learning, and artificial intelligence in echocardiography: A brave new world [J]. Echocardiography, 2018, 35(9): 1402-1418.
13
Medvedofsky D, Mor-Avi V, Kruse E, et al. Quantification of right ventricular size and function from contrast-enhanced three-dimensional echocardioimagedata images [J]. J Am Soc Echocardiogr, 2017, 30(12): 1193-1202.
14
Nochioka K, Roca GQ, Claggett B, et al. Right ventricular function, right ventricular-pulmonary artery coupling, and heart failure risk in 4 US communities The Atherosclerosis Risk in Communities (ARIC) Study [J]. JAMA Cardiol, 2018, 3(10): 939-948.
15
Genovese D, Rashedi N, Weinert L, et al. Machine learning-based three-dimensional echocardioimagedata quantification of right ventricular size and function: validation against cardiac magnetic resonance [J]. J Am Soc Echocardiogr, 2019, 32(8): 969-977.
16
Zhu Y, Bao YW, Zheng KC, et al. Quantitative assessment of right ventricular size and function with multiple parameters from artificial intelligence-based three-dimensional echocardiography: A comparative study with cardiac magnetic resonance [J]. Echocardiography, 2022, 39(2): 223-232.
17
Schneider M, Bartko P, Geller W, et al. A machine learning algorithm supports ultrasound-naive novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF [J]. Int J Cardiovasc Imaging, 2021, 37(2): 577-586.
18
Asch FM, Poilvert N, Abraham T, et al. Automated echocardioimagedata 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.
19
Lang R, Badano L, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging [J]. J Am Soc Echocardiogr, 2015, 28(1): 1-39.
20
Salte IM, Ostvik A, Smistad E, et al. Artificial intelligence for automatic measurement of left ventricular strain in echocardiography [J]. JACC Cardiovasc Imaging, 2021, 14(10): 1918-1928.
21
Karuzas A, Sablauskas K, Verikas D, et al. Accurate prediction of left ventricular diastolic dysfunction in 2D echocardiography using ensemble of deep convolutional neural networks [J]. Eur Heart J, 2020, 41(2): 3436.
22
Jiang R, Yeung D, Behnami D, et al. A novel continuous left ventricular diastolic function score using machine learning [J]. J Am Soc Echocardiogr, 2022, 35(12): 1247-1255.
23
Choi DJ, Park JJ, Ali T, et al. Artificial intelligence for the diagnosis of heart failure [J]. NPJ Digit Med, 2020, 3(54): 1-6.
24
Nedadur R, Wang B, Tsang W. Artificial intelligence for the echocardioimagedata assessment of valvular heart disease [J]. Heart, 2022, 108(20): 1592-1599.
25
Chandra V, Sarkar PG, Singh V. Mitral valve leaflet tracking in echocardiography using custom Yolo3 [J]. Procedia Computer Sci, 2020, 171(12): 820-828.
26
Sengupta PP, Shrestha S, Kagiyama N, et al. A machine-learning framework to identify distinct phenotypes of aortic stenosis severity [J]. JACC Cardiovasc Imaging, 2021, 14(9): 1707-1720.
27
Moghaddasi H, Nourian S. Automatic assessment of mitral regurgitation severity based on extensive textural features on 2D echocardiography videos [J]. Comput Biol Med, 2016, 73(1): 47-55.
28
Yu F, Huang HB, Yu QH, et al. Artificial intelligence-based myocardial texture analysis in etiological differentiation of left ventricular hypertrophy [J]. Ann Transl Med, 2021, 9(2): 108-126.
29
Duffy G, Cheng PP, Yuan N, et al. High-throughput precision phenotyping of left ventricular hypertrophy with cardiovascular deep learning [J]. JAMA Cardiol, 2022, 7(4): 386-395.
30
Sengupta PP, Huang YM, Bansal M, et al. Cognitive machine-learning algorithm for cardiac imaging a pilot study for differentiating constrictive pericarditis from restrictive cardiomyopathy [J]. Circ Cardiovasc Imaging, 2016, 9(6): e004330.
31
Kusunose K, Abe T, Haga A, et al. A deep learning approach for assessment of regional wall motion abnormality from echocardioimagedata images [J]. JACC Cardiovasc Imaging, 2020, 13(2): 374-381.
32
Omar HA, Domingos JS, Patra A, et al. Quantification of cardiac bull's-eye map based on principal strain analysis for myocardial wall motion assessment in stress echocardiography: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) [C]. Washington DC, 2018.
33
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 Cardiovas Imaging, 2021, 37(6): 1967-1978.
34
Tian M, Zheng M, Qiu S, et al. A prediction model of microcirculation disorder in myocardium based on ultrasonic images [J]. J Amb Intel Hum Comp, 2023, 14(6): 7319-7330.
35
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.
No related articles found!
阅读次数
全文


摘要