人工智能辅助测量心肌应变的研究进展

doi:10.3969/j.issn.1674-8115.2024.06.013

上海交通大学学报（医学版） ›› 2024, Vol. 44 ›› Issue (6): 773-778.doi: 10.3969/j.issn.1674-8115.2024.06.013

人工智能辅助测量心肌应变的研究进展

李昕欣(), 边懿泽(), 赵航, 姜萌()

上海交通大学医学院附属仁济医院心内科，上海 200127

收稿日期:2024-01-19 接受日期:2024-03-06 出版日期:2024-06-28 发布日期:2024-06-28
通讯作者: 姜萌 E-mail:520710910006@shsmu.edu.cn;520710910012@shsmu.edu.cn;jiangmeng0919@163.com
作者简介:李昕欣（2002—），女，本科生；电子信箱：520710910006@shsmu.edu.cn
边懿泽（2002—），女，本科生；电子信箱:520710910012@shsmu.edu.cn第一联系人：（李昕欣、边懿泽并列第一作者）
基金资助:
国家自然科学基金(U21A20341);上海市“科技创新行动计划”优秀技术带头人计划(21XD1432100);上海申康医院发展中心促进市级医院临床技能与临床创新能力三年行动计划(SHDC2020CR2025B);上海交通大学“交大之星”计划医工交叉研究基金(YG2019ZDA13)

Research progress in the artificial intelligence-assisted measurement of myocardial strain

LI Xinxin(), BIAN Yize(), ZHAO Hang, JIANG Meng()

Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

Received:2024-01-19 Accepted:2024-03-06 Online:2024-06-28 Published:2024-06-28
Contact: JIANG Meng E-mail:520710910006@shsmu.edu.cn;520710910012@shsmu.edu.cn;jiangmeng0919@163.com
Supported by:
National Natural Science Foundation of China(U21A20341);Excellent Technology Leader Program Project of "Science and Technology Innovation Action Plan" in Shanghai(21XD1432100);Shanghai Shenkang Hospital Development Center Three-Year Action Plan: Promoting Clinical Skills and Innovation in Municipal Hospital(SHDC2020CR2025B);Medical-Engineering Cross Research of Shanghai Jiao Tong University(YG2019ZDA13)

摘要/Abstract

摘要：

心肌应变是反映应力作用下整体或局部心肌形变程度的无量纲参数，可以量化检测心肌损伤，指导心脏疾病的早期诊断、干预与预后评估。心脏超声、心脏CT、心脏磁共振均可被用来进行应变成像与分析，其中二维斑点追踪超声心动图是当下应用最为广泛的心肌应变检测手段。但由于人工分析心肌应变存在观察者间差异且所使用的成像系统和分析软件各有不同，测得的应变值在不同供应商中一致性和可重复性欠佳，限制了心肌应变参数的临床应用。而人工智能可以通过自动应变计算和图像质量评估等方式在一定程度上克服应变测量的缺陷，具备广阔的发展前景。该文重点介绍人工智能在超声、磁共振等影像学手段中辅助测量心肌应变的研究进展，及其在疾病诊断与患者预后评估中的应用，希望助力于提高应变测量的效率和一致性，推动心肌应变常规应用于临床，在心肌损伤及心功能评估中发挥增量作用。然而，目前大部分研究涉及的样本量较小，并且缺乏外部验证，其结果的可靠性还需进一步证实。

关键词: 心肌应变, 人工智能, 心肌损伤

Abstract:

Myocardial strain is a dimensionless parameter reflecting the degree of deformation of the whole or local myocardium under stress, which can quantitatively detect myocardial injury and guide the early diagnosis, intervention and prognostic assessment of cardiac diseases. Cardiac ultrasound, cardiac CT and cardiac magnetic resonance can all be used for strain imaging and analysis, with two-dimensional speckle-tracking echocardiography being the most widely used means of myocardial strain detection today. However, due to inter-observer variations in manual analysis of myocardial strain and differences in the imaging systems and analysis software, the consistency and reproducibility of measured strain values among vendors are poor, limiting the clinical application of myocardial strain. Artificial intelligence (AI) can overcome the defects of strain measurement to a certain extent through automatic strain calculation and image quality assessment, which has a broad developmental prospect. This review focuses on the progress of AI-assisted measurement of myocardial strain in ultrasound, magnetic resonance, and other imaging modalities, as well as its application to disease diagnosis and patient prognosis assessment. This will improve the efficiency and consistency of strain measurement and promote the routine application of myocardial strain to clinical practice, which will play an incremental role in assessing myocardial injury and cardiac function. However, most of the current studies involve small sample sizes and lack external validation, and the reliability of their results needs to be further verified.

Key words: myocardial strain, artificial intelligence (AI), myocardial damage

中图分类号:

R542.2

李昕欣, 边懿泽, 赵航, 姜萌. 人工智能辅助测量心肌应变的研究进展[J]. 上海交通大学学报（医学版）, 2024, 44(6): 773-778.

LI Xinxin, BIAN Yize, ZHAO Hang, JIANG Meng. Research progress in the artificial intelligence-assisted measurement of myocardial strain[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(6): 773-778.

参考文献 45

1	PURWOWIYOTO S L, HALOMOAN R. Highlighting the role of global longitudinal strain assessment in valvular heart disease[J]. Egypt Heart J, 2022, 74(1): 46.
2	THAVENDIRANATHAN P, POULIN F, LIM K D, et al. Use of myocardial strain imaging by echocardiography for the early detection of cardiotoxicity in patients during and after cancer chemotherapy: a systematic review[J]. J Am Coll Cardiol, 2014, 63(25 Pt A): 2751-2768.
3	FARSALINOS K E, DARABAN A M, ÜNLÜ S, et al. Head-to-head comparison of global longitudinal strain measurements among nine different vendors: the EACVI/ASE inter-vendor comparison study[J]. J Am Soc Echocardiogr, 2015, 28(10): 1171-1181, e2.
4	NEGISHI K, LUCAS S, NEGISHI T, et al. What is the primary source of discordance in strain measurement between vendors: imaging or analysis?[J]. Ultrasound Med Biol, 2013, 39(4): 714-720.
5	GHADIMI S, AUGER D A, FENG X, et al. Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping[J]. J Cardiovasc Magn Reson, 2021, 23(1): 20.
6	SALTE I M, ØSTVIK 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.
7	MIRSKY I, PARMLEY W W. Assessment of passive elastic stiffness for isolated heart muscle and the intact heart[J]. Circ Res, 1973, 33(2): 233-243.
8	SENGUPTA P P, NARULA J. Cardiac strain as a universal biomarker: interpreting the sounds of uneasy heart muscle cells[J]. JACC Cardiovasc Imaging, 2014, 7(5): 534-536.
9	ERNANDE L, THIBAULT H, BERGEROT C, et al. Systolic myocardial dysfunction in patients with type 2 diabetes mellitus: identification at MR imaging with cine displacement encoding with stimulated echoes[J]. Radiology, 2012, 265(2): 402-409.
10	NIU J Q, ZENG M, WANG Y, et al. Sensitive marker for evaluation of hypertensive heart disease: extracellular volume and myocardial strain[J]. BMC Cardiovasc Disord, 2020, 20(1): 292.
11	AARSAETHER E, RÖSNER A, STRAUMBOTN E, et al. Peak longitudinal strain most accurately reflects myocardial segmental viability following acute myocardial infarction: an experimental study in open-chest pigs[J]. Cardiovasc Ultrasound, 2012, 10: 23.
12	MANGION K, MCCOMB C, AUGER D A, et al. Magnetic resonance imaging of myocardial strain after acute ST-segment-elevation myocardial infarction: a systematic review[J]. Circ Cardiovasc Imaging, 2017, 10(8): e006498.
13	HELM R H, BYRNE M, HELM P A, et al. Three-dimensional mapping of optimal left ventricular pacing site for cardiac resynchronization[J]. Circulation, 2007, 115(8): 953-961.
14	LIM P, BUAKHAMSRI A, POPOVIC Z B, et al. Longitudinal strain delay index by speckle tracking imaging: a new marker of response to cardiac resynchronization therapy[J]. Circulation, 2008, 118(11): 1130-1137.
15	DENG L. Artificial intelligence in the rising wave of deep learning: the historical path and future outlook[perspectives][J]. IEEE Signal Process Mag, 2018, 35(1): 180,173-177.
16	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
17	RONG G G, MENDEZ A, BOU ASSI E, et al. Artificial intelligence in healthcare: review and prediction case studies[J]. Engineering, 2020, 6(3): 291-301.
18	HSIAO H C W, CHEN S H F, TSAI J J P. Deep learning for risk analysis of specific cardiovascular diseases using environmental data and outpatient records[C/OL]//2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan, China. IEEE, 2016: 369-372[2023-12-19].https://ieeexplore.ieee.org/document/7790013.
19	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.
20	COTELLA J I, SLIVNICK J A, SANDERSON E, et al. Artificial intelligence based left ventricular ejection fraction and global longitudinal strain in cardiac amyloidosis[J]. Echocardiography, 2023, 40(3): 188-195.
21	BACKHAUS S J, ALDEHAYAT H, KOWALLICK J T, et al. Artificial intelligence fully automated myocardial strain quantification for risk stratification following acute myocardial infarction[J]. Sci Rep, 2022, 12(1): 12220.
22	HEIMDAL A, STØYLEN A, TORP H, et al. Real-time strain rate imaging of the left ventricle by ultrasound[J]. J Am Soc Echocardiogr, 1998, 11(11): 1013-1019.
23	PIRAT B, KHOURY D S, HARTLEY C J, et al. A novel feature-tracking echocardiographic method for the quantitation of regional myocardial function: validation in an animal model of ischemia-reperfusion[J]. J Am Coll Cardiol, 2008, 51(6): 651-659.
24	MOR-AVI V, LANG R M, BADANO L P, et al. Current and evolving echocardiographic techniques for the quantitative evaluation of cardiac mechanics: ASE/EAE consensus statement on methodology and indications endorsed by the Japanese Society of Echocardiography[J]. Eur J Echocardiogr, 2011, 12(3): 167-205.
25	PÉREZ DE ISLA L, BALCONES D V, FERNÁNDEZ-GOLFÍN C, et al. Three-dimensional-wall motion tracking: a new and faster tool for myocardial strain assessment: comparison with two-dimensional-wall motion tracking[J]. J Am Soc Echocardiogr, 2009, 22(4): 325-330.
26	KAWAKAMI H, WRIGHT L, NOLAN M, et al. Feasibility, reproducibility, and clinical implications of the novel fully automated assessment for global longitudinal strain[J]. J Am Soc Echocardiogr, 2021, 34(2): 136-145.e2.
27	EVAIN E, SUN Y Y, FARAZ K, et al. Motion estimation by deep learning in 2D echocardiography: synthetic dataset and validation[J]. IEEE Trans Med Imaging, 2022, 41(8): 1911-1924.
28	LIU S, BOSE, AHMED A, et al. Artificial intelligence-based assessment of indices of right ventricular function[J]. J Cardiothorac Vasc Anesth, 2020, 34(10): 2698-2702.
29	HUANG K C, HUANG C S, SU M Y, et al. Artificial intelligence aids cardiac image quality assessment for improving precision in strain measurements[J]. JACC Cardiovasc Imaging, 2021, 14(2): 335-345.
30	SCATTEIA A, BARITUSSIO A, BUCCIARELLI-DUCCI C. Strain imaging using cardiac magnetic resonance[J]. Heart Fail Rev, 2017, 22(4): 465-476.
31	AXEL L, MONTILLO A, KIM D. Tagged magnetic resonance imaging of the heart: a survey[J]. Med Image Anal, 2005, 9(4): 376-393.
32	ZERHOUNI E A, PARISH D M, ROGERS W J, et al. Human heart: tagging with MR imaging‒ a method for noninvasive assessment of myocardial motion[J]. Radiology, 1988, 169(1): 59-63.
33	CLAUS P, OMAR A M S, PEDRIZZETTI G, et al. Tissue tracking technology for assessing cardiac mechanics: principles, normal values, and clinical applications[J]. JACC Cardiovasc Imaging, 2015, 8(12): 1444-1460.
34	GRÖSCHEL J, KUHNT J, VIEZZER D, et al. Comparison of manual and artificial intelligence based quantification of myocardial strain by feature tracking: a cardiovascular MR study in health and disease[J]. Eur Radiol, 2024, 34(2): 1003-1015.
35	CHENG N N, BONAZZOLA R, RAVIKUMAR N, et al. A generative framework for predicting myocardial strain from cine-cardiac magnetic resonance imaging[C]//Annual Conference on Medical Image Understanding and Analysis. Cham: Springer, 2022: 482-493.
36	MISKINYTE E, BUCIUS P, ERLEY J, et al. Assessment of global longitudinal and circumferential strain using computed tomography feature tracking: intra-individual comparison with CMR feature tracking and myocardial tagging in patients with severe aortic stenosis[J]. J Clin Med, 2019, 8(9): 1423.
37	WONG K C, TEE M, CHEN M, et al. Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach[J]. Int J Comput Assist Radiol Surg, 2016, 11(9): 1573-1583.
38	KOEHLER S, KUHM J, HUFFAKER T, et al. Artificial intelligence to derive aligned strain in cine CMR to detect patients with myocardial fibrosis: an open and scrutinizable approach[J]. Res Sq, 2024.DOI: 10.21203/rs.3.rs-3785677/v1.
39	KARAGODIN I, CARVALHO SINGULANE C, WOODWARD G M, et al. Echocardiographic correlates of in-hospital death in patients with acute COVID-19 infection: the world alliance societies of echocardiography (WASE-COVID) study[J]. J Am Soc Echocardiogr, 2021, 34(8): 819-830.
40	O'DRISCOLL J M, 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.
41	AKERMAN A, BERNARD L, DESCHAMPS T, et al. Automated contouring of non-contrast echocardiograms result in similar estimates of left ventricular function to manually contoured contrast-enhanced images in chemotherapy patients[J]. Eur Heart J Cardiovasc Imaging, 2022, 23(Suppl 1): jeab289.013.
42	OUYANG D, HE B, GHORBANI A, et al. Video-based AI for beat-to-beat assessment of cardiac function[J]. Nature, 2020, 580(7802): 252-256.
43	NARULA S, SHAMEER K, SALEM OMAR A M, et al. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography[J]. J Am Coll Cardiol, 2016, 68(21): 2287-2295.
44	World Health Organization. Ethics and governance of artificial intelligence for health[M]. Geneva: World Health Organization, 2021.
45	隗冰芮, 薛鹏, 江宇, 等. 世界卫生组织《医疗卫生中人工智能的伦理治理》指南及对中国的启示[J]. 中华医学杂志, 2022, 102(12): 833-837.
	KUI B R, XUE P, JIANG Y, et al. World Health Organization guidance Ethical and Governance of Artificial Intelligence for Health and implications for China[J]. National Medical Journal of China, 2022, 102(12): 833-837.

[1]	周天凡, 邵飞雪, 万盛, 周晨晨, 周思锦, 花晓琳. 基于人工智能模型量化视网膜血管特征参数预测子痫前期的可行性研究[J]. 上海交通大学学报（医学版）, 2024, 44(5): 552-559.
[2]	郭勇麟, 陈墨馨, 刘哲源, 李奕霏, 王子琦, 舒琴, 李琳. 基于人工智能技术的斜视诊疗进展[J]. 上海交通大学学报（医学版）, 2024, 44(3): 393-398.
[3]	马奔, 赵成, 束翌俊, 董平. CT影像组学在胃肠道间质瘤中的应用进展[J]. 上海交通大学学报（医学版）, 2023, 43(7): 923-930.
[4]	卫雪敏, 高成金. ASPECT评分在急性缺血性脑卒中临床应用中的研究进展[J]. 上海交通大学学报（医学版）, 2022, 42(7): 919-924.
[5]	王晓峰, 周璐, 姚乐宇, 何凡, 彭海霞, 杨大明, 黄晓霖. 深度卷积神经网络模型辅助下结直肠息肉检测系统对初级医师结直肠小息肉检出率的影响[J]. 上海交通大学学报(医学版), 2022, 42(2): 205-210.
[6]	刘想, 谢辉辉, 许玉峰, 陶晓峰, 柳林, 吴迪嘉, 王霄英. 人工智能在胸部创伤肋骨骨折CT诊断中应用的初步研究[J]. 上海交通大学学报(医学版), 2021, 41(7): 920-925.
[7]	李欢, 易培强, 苏筠, 陈培战, 许赬, 曹璐, 陈佳艺, 李敏. 垂体腺苷酸环化酶激活肽38对急性放射性心肌损伤的防护作用[J]. 上海交通大学学报(医学版), 2021, 41(2): 129-133.
[8]	李小敏, 曲扬, 张少霆, 赵亮, 刘畅, 谢帅宁, 戴尅戎, 艾松涛. 人工智能技术在骨肌系统影像学方面的应用[J]. 上海交通大学学报(医学版), 2021, 41(2): 262-266.
[9]	张璐1，陈强2，蒋蓓蓓1，丁珍红2，张丽3，解学乾1. 基于生成式对抗网络的冠状动脉CT血管成像运动伪影去除的初步研究[J]. 上海交通大学学报(医学版), 2020, 40(9): 1229-1235.
[10]	荣雯雯1，汪刚1，朱其立2. 基于人工智能的病历后结构化专病数据库在临床研究中的价值探讨[J]. 上海交通大学学报(医学版), 2020, 40(7): 995-1000.
[11]	陆言巧，沈兰，何奔. 人工智能在心血管疾病的辅助诊疗中的应用[J]. 上海交通大学学报（医学版）, 2020, 40(2): 259-.
[12]	林艺彤，汪海娅. 二维斑点追踪超声评价餐后低血压对左室心肌应变的影响[J]. 上海交通大学学报（医学版）, 2016, 36(6): 884-.
[13]	孙瑛, 朱明, 张剑蔚, 等. 七氟醚预处理和后处理对婴幼儿体外循环心肌再灌注损伤的影响[J]. , 2011, 31(9): 1316-.
[14]	胡萌, 蒋慧, 夏敏. RAC2基因遗传多态性与柔红霉素引起心肌损伤的相关性研究[J]. , 2011, 31(1): 47-.
[15]	刁雪红, 申锷, 胡兵, 等. 糖尿病小鼠心肌组织microRNA表达谱分析[J]. , 2010, 30(10): 1194-.

人工智能辅助测量心肌应变的研究进展

Research progress in the artificial intelligence-assisted measurement of myocardial strain

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献 45

相关文章 15

编辑推荐

Metrics