Journal of Shanghai Jiao Tong University (Medical Science) ›› 2024, Vol. 44 ›› Issue (6): 773-778.doi: 10.3969/j.issn.1674-8115.2024.06.013
• Review • Previous Articles Next Articles
LI Xinxin(), BIAN Yize(), ZHAO Hang, JIANG Meng()
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:
CLC Number:
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.
Add to citation manager EndNote|Ris|BibTeX
URL: https://xuebao.shsmu.edu.cn/EN/10.3969/j.issn.1674-8115.2024.06.013
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] | ZHOU Tianfan, SHAO Feixue, WAN Sheng, ZHOU Chenchen, ZHOU Sijin, HUA Xiaolin. Feasibility study on quantifying retinal vascular features for predicting preeclampsia based on artificial intelligence models [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2024, 44(5): 552-559. |
[2] | MA Ben, ZHAO Cheng, SHU Yijun, DONG Ping. Application progress of CT radiomics in gastrointestinal stromal tumor [J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(7): 923-930. |
[3] | Xiaofeng WANG, Lu ZHOU, Leyu YAO, Fan HE, Haixia PENG, Daming YANG, Xiaolin HUANG. Effect of DCNN model-assisted colorectal polyp detection system on the detection of colorectal polyps by junior physicians [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2022, 42(2): 205-210. |
[4] | RONG Wen-wen1, WANG Gang1, ZHU Qi-li2. Discussion on value of medical records-structured specialized disease database based on artificial intelligence in clinical research [J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2020, 40(7): 995-1000. |
[5] | LU Yan-qiao, SHEN Lan, HE Ben. Application of artificial intelligence in assisted diagnosis and treatment of cardiovascular disease [J]. , 2020, 40(2): 259-. |
[6] | MIAO Yu-tong1, SHEN Lan1, 2, HE Ben1. Imaging evaluation of post-myocardial infarction injury [J]. , 2019, 39(4): 436-. |
[7] | LIN Yi-tong, WANG Hai-ya. Evaluation of the effect of postprandial hypotension on left ventricular myocardial strain by 2D speckle tracking imaging [J]. , 2016, 36(6): 884-. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||