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 (01) : 118 -121. doi: 10.3877/cma.j.issn.1672-6448.2023.01.020

综述

人工智能在超声心动图图像质量控制中的应用现状和发展前景
李明奇1, 周青1,()   
  1. 1. 430060 武汉,武汉大学人民医院超声影像科
  • 收稿日期:2022-05-24 出版日期:2023-01-01
  • 通信作者: 周青
  • 基金资助:
    国家自然科学基金项目(82271999)

Application status and development prospects of artificial intelligence in echocardiographic image quality control

Mingqi Li1, Qing Zhou1()   

  • Received:2022-05-24 Published:2023-01-01
  • Corresponding author: Qing Zhou
引用本文:

李明奇, 周青. 人工智能在超声心动图图像质量控制中的应用现状和发展前景[J]. 中华医学超声杂志(电子版), 2023, 20(01): 118-121.

Mingqi Li, Qing Zhou. Application status and development prospects of artificial intelligence in echocardiographic image quality control[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(01): 118-121.

表1 基于深度学习的超声心动图自动图像质量评价的研究汇总
作者 年份 输入切面 图像评分标签标准 输出与表现
Abdi等16 2017 A4C

0分:仅主动脉瓣和/(或)房间隔或室间隔可见

1分:1或2个腔室边界可见

2分:3个腔室边界可见

3分:3或4个腔室边界可见,但未达到量化测量要求;心脏偏轴或明显缩短

4分:3或4个腔室的边界清晰,符合量化测量要求,心脏无偏轴,但轻度缩短

5分:4个腔室的边界清晰,心脏位于合适的轴线,边缘锐利,无缩短

输出:总体质量评分

神经网络表现:绝对误差为0.71±0.58

Abdi等17 2017 A2C,A3C,A4C,PLAXA,PLAXPM

整体评估:0分,无结构显示或不足以进行评估;1分,可观察到结构;2分,结构显示效果佳

细节评估:适当的焦点(1分);正确的深度设置(0.5分);适当的增益(0.5分);正确的轴向(1分)

输出:总体质量评分

神经网络表现:平均质量评分的准确率为85%

Dong等19 2019 胎儿四腔心切面

胎儿四腔心切面:四腔心结构清晰+4分

增益:部分回声过高或整体回声过低-1分,否则+1分

放大:胸部轮廓占扇扫范围的2/3则+1分,否则-1分

肺静脉:左心房与脊柱之间可见清晰肺静脉+2分

心尖部及调节束:心尖部清晰,调节束表现为高回声+2分

肋骨:在一侧或两侧可见≥2根肋骨-1分

输出:6个评价指标分别得分

神经网络表现:内部验证平均精确度为93.52%

PASCAL数据库中平均精确度率为81.2%

Vrettos等15 2020 A4C

轴向:明显偏轴0分,轻度偏轴1分,轴向正确2分

对比度/增益:不当0分,尚可1分,最佳2分

左心室短缩:明显短缩0分,轻度短缩1分,无短缩2分

输出:3个评价指标分别得分

神经网络表现:平均准确率为86%

Luong等18 2021 PLAX,A2C,A3C,A4C,PSAXA,PSAXM,PSAXPM,SC4,IVC

1分:>75%的预期的血池-组织界面清晰显示

0.75分:51%~75%的预期的血池-组织界面清晰显示

0.5分:26%~50%的预期的血池-组织界面清晰显示

0.25分:<25%的预期的血池-组织界面清晰显示

输出:总体质量评分

神经网络表现:绝对误差为0.12±0.09

1
Donofrio MT, Moon-Grady AJ, Hornberger LK, et al. Diagnosis and treatment of fetal cardiac disease: a scientific statement from the American Heart Association [J]. Circulation, 2014, 129(21): 2183-2242.
2
Feldman MK, Katyal S, Blackwood MS. US artifacts [J]. Radiographics, 2009, 29(4): 1179-1189.
3
Papolos A, Narula J, Bavishi C, et al. U.S. Hospital use of echocardiography: insights from the nationwide inpatient sample [J]. J Am Coll Cardiol, 2016, 67(5): 502-511.
4
Deo RC. Machine learning in medicine [J]. Circulation, 2015, 132(20): 1920-1930.
5
LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.
6
Litjens G, Ciompi F, Wolterink JM, et al. State-of-the-art deep learning in cardiovascular image analysis [J]. JACC Cardiovasc Imaging, 2019, 12(8 Pt 1): 1549-1565.
7
Schuuring MJ, Išgum I, Cosyns B, et al. Routine echocardiography and artificial intelligence solutions [J]. Front Cardiovasc Med, 2021, 8: 648877.
8
Shad R, Quach N, Fong R, et al. Predicting post-operative right ventricular failure using video-based deep learning [J]. Nat Commun, 2021, 12(1): 5192.
9
Popescu BA, Stefanidis A, Fox KF, et al. Training, competence, and quality improvement in echocardiography: the European Association of Cardiovascular Imaging Recommendations: update 2020 [J]. Eur Heart J Cardiovasc Imaging, 2020, 21(12): 1305-1319.
10
Østvik 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.
11
Zhang J, Gajjala S, Agrawal P, et al. Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy [J]. Circulation, 2018, 138(16): 1623-1635.
12
Narang A, Bae R, Hong H, et al. Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use [J]. JAMA Cardiol, 2021, 6(6): 624-632.
13
Snare SR, Torp H, Orderud F, et al. Real-time scan assistant for echocardiography [J]. IEEE Trans Ultrason Ferroelectr Freq Control, 2012, 59(3): 583-589.
14
Pavani SK, Subramanian N, Das Gupta M, et al. Quality metric for Parasternal Long AXis B-mode echocardiograms [J]. Med Image Comput Comput Assist Interv, 2012, 15(Pt 2): 478-485.
15
Vrettos A, Azarmehr N, Howard JP, et al. Automated assessment of image quality in 2D echocardiography using deep learning [C]. International Conference on Radiology, Medical Imaging and Radiation Oncology ICRMIRO, Paris, France: 2020.
16
Abdi AH, Luong C, Tsang T, et al. Automatic quality assessment of echocardiograms using convolutional neural networks: feasibility on the apical four-chamber view [J]. IEEE Trans Med Imaging, 2017, 36(6): 1221-1230.
17
Abdi AH, Luong C, Tsang T, et al. Quality assessment of echocardiographic cine using recurrent neural networks: feasibility on five standard view planes [C]. Medical Image Computing and Computer Assisted Intervention, Cham: Springer, 2017.
18
Luong C, Liao Z, Abdi A, et al. Automated estimation of echocardiogram image quality in hospitalized patients [J]. Int J Cardiovasc Imaging, 2021, 37(1): 229-239.
19
Dong J, Liu S, Liao Y, et al. A generic quality control framework for fetal ultrasound cardiac four-chamber planes [J]. IEEE J Biomed Health Inform, 2020, 24(4): 931-942.
20
Porter TR, Mulvagh SL, Abdelmoneim SS, et al. Clinical applications of ultrasonic enhancing agents in echocardiography: 2018 American Society of Echocardiography Guidelines Update [J]. J Am Soc Echocardiogr, 2018, 31(3): 241-274.
21
Senior R, Becher H, Monaghan M, et al. Clinical practice of contrast echocardiography: recommendation by the European Association of Cardiovascular Imaging (EACVI) 2017 [J]. Eur Heart J Cardiovasc Imaging, 2017, 18(11): 1205-1205af.
22
朱天刚, 靳文英, 张梅,等. 心脏超声增强剂临床应用规范专家共识 [J/CD]. 中华医学超声杂志(电子版), 2019, 16(10): 731-734.
23
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.
24
李明奇, 费洪文. 人工智能在心肌声学造影中的应用现状及发展趋势 [J]. 临床超声医学杂志, 2020, 22(7): 526-528.
25
Ding Y, Zeng D, Li M, et al. Towards Efficient human-machine collaboration: real-time correction effort prediction for ultrasound data acquisition [C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 2021: 461-470.
26
Porter TR, Abdelmoneim S, Belcik JT, et al. Guidelines for the cardiac sonographer in the performance of contrast echocardiography: a focused update from the American Society of Echocardiography [J]. J Am Soc Echocardiogr, 2014, 27(8): 797-810.
27
Cosyns B, Helfen A, Leong-Poi H, et al. How to perform an ultrasound contrast myocardial perfusion examination? [J]. Eur Heart J Cardiovasc Imaging, 2022, 23(6): 727-729.
28
吴爵非, 彭冠华, 张建琴,等. 左心室和心肌声学造影的仪器设置与方法学 [J/CD]. 中华医学超声杂志(电子版), 2019, 16(10): 727-730.
No related articles found!
阅读次数
全文


摘要