A preliminary exploration of a deep learning-based artificial intelligence model for automatic quantification of echocardiographic left ventricular ejection fraction

HE Lan; LU Yang; XIA Zhigang; XIE Xiaoyi; DU Lili; GU Shulian; MA Lan; HE Yongming; SHEN E

doi:10.7619/jcmp.20240289

Journal of Clinical Medicine in Practice > 2024 > 28(9): 9-14. > DOI: 10.7619/jcmp.20240289

HE Lan, LU Yang, XIA Zhigang, XIE Xiaoyi, DU Lili, GU Shulian, MA Lan, HE Yongming, SHEN E. A preliminary exploration of a deep learning-based artificial intelligence model for automatic quantification of echocardiographic left ventricular ejection fraction[J]. Journal of Clinical Medicine in Practice, 2024, 28(9): 9-14. DOI: 10.7619/jcmp.20240289

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A preliminary exploration of a deep learning-based artificial intelligence model for automatic quantification of echocardiographic left ventricular ejection fraction

1.
Department of Ultrasound, Shanghai Chest Hospital, Chest Hospital Affiliated to School of Medicine of Shanghai Jiao Tong University, Shanghai, 200030
2.
Shanghai Aitrox Technology Corporation Limited, Shanghai, 200032
3.
Division of Cardiology, the First Hospital Affiliated to Soochow University, Suzhou, Jiangsu, 215006

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Received Date: January 15, 2024
Revised Date: February 29, 2024
Available Online: May 14, 2024

Graphical Abstract

Abstract

Abstract

Objective
To construct a deep learning-based artificial intelligence model to automatically quantify left ventricular ejection fraction (LVEF) using static views of echocardiography.
Methods
The study included data of 1, 902 adults with left ventricular multi-slice echocardiographic views at end-systole and end-diastole. The collected dataset was divided into development set (1, 610 cases, with 1, 252 cases for model training and 358 cases for parameter adjustment), internal test set (177 cases for internal validation), and external test set (115 cases for external validation and generalization testing). The model achieved left ventricular segmentation and automatic quantification of LVEF through precise identification of the left ventricular endocardial boundary and inspection of key points. The Dice coefficient was employed to evaluate the performance of the left ventricular segmentation model, while the Pearson correlation coefficient and the intraclass correlation coefficient were used to assess the correlation and consistency between the automatically measured LVEF and the reference standard.
Results
The left ventricular segmentation model performed well, with Dice coefficients ≥ 0.90 for both the internal and external independent test sets; the agreement between the automatically measured LVEF and the cardiologists' manual measurements was moderate, with Pearson correlation coefficients ranging from 0.46 to 0.71 and intragroup correlation analysis agreements from 0.39 to 0.57 for the internal test set; and Pearson correlation coefficients for the independent external test set were 0.26 to 0.54 and intra-group correlation analysis agreement of 0.23 to 0.50.
Conclusion
In this study, a left ventricular segmentation model with better performance is constructed, and initial application of the model for automatic quantification of LVEF for two-dimensional echocardiography has general performance, which requires further optimisation of the algorithm to improve the model generalisation.
- deep learning,
- echocardiography,
- left ventricular ejection fraction,
- automatic segmentation,
- performance optimization for model

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References (21)

References

[1]	SAVARESE G, BECHER P M, LUND L H, et al. Global burden of heart failure: a comprehensive and updated review of epidemiology[J]. Cardiovasc Res, 2023, 118(17): 3272-3287. doi: 10.1093/cvr/cvac013
[2]	AGGARWAL R, YEH R W, JOYNT MADDOX K E, et al. Cardiovascular risk factor prevalence, treatment, and control in US adults aged 20 to 44 years, 2009 to March 2020[J]. JAMA, 2023, 329(11): 899-909. doi: 10.1001/jama.2023.2307
[3]	SHAH K S, XU H L, MATSOUAKA R A, et al. Heart failure with preserved, borderline, and reduced ejection fraction: 5-year outcomes[J]. J Am Coll Cardiol, 2017, 70(20): 2476-2486. doi: 10.1016/j.jacc.2017.08.074
[4]	KOH A S, TAY W T, TENG T H K, et al. A comprehensive population-based characterization of heart failure with mid-range ejection fraction[J]. Eur J Heart Fail, 2017, 19(12): 1624-1634. doi: 10.1002/ejhf.945
[5]	湛先发, 余小亚, 王洪军, 等. 3种机器学习算法评估脑梗死患者颈动脉斑块稳定性的效能比较[J]. 实用临床医药杂志, 2023, 27(22): 6-12. doi: 10.7619/jcmp.20232657
[6]	裴昌军, 孙雪丽, 王鑫, 等. 人工智能结合多层螺旋CT检查在机关体检人群肺结节筛查中的应用[J]. 实用临床医药杂志, 2023, 27(24): 89-92. doi: 10.7619/jcmp.20232282
[7]	曾研. 医学超声若干目标检测深度学习方法研究[D]. 北京: 北京工业大学, 2022.
[8]	张浩, 常建东. 基于文献计量方法的人工智能在超声心动图中的应用进展研究[J]. 中国医疗设备, 2023, 38(1): 127-133. https://www.cnki.com.cn/Article/CJFDTOTAL-YLSX202301024.htm
[9]	中华医学会超声医学分会超声心动图学组. 中国成年人超声心动图检查测量指南[J]. 中华超声影像学杂志, 2016, 25(8): 645-666. https://cdmd.cnki.com.cn/Article/CDMD-10632-1018178662.htm
[10]	LANG R M, BADANO L P, 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]. Eur Heart J Cardiovasc Imaging, 2015, 16(3): 233-270. doi: 10.1093/ehjci/jev014
[11]	LINDENHEIM-LOCHER W, SWITONSKI A, KRZESZOWSKI T, et al. YOLOv5 drone detection using multimodal data registered by the vicon system[J]. Sensors, 2023, 23(14): 6396. doi: 10.3390/s23146396
[12]	LIU X, FAN Y T, LI S, et al. Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography[J]. Am J Physiol Heart Circ Physiol, 2021, 321(2): H390-H399. doi: 10.1152/ajpheart.00416.2020
[13]	XIE S N, GIRSHICK R, DOLLAR P, et al. Aggregated residual transformations for deep neural networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017: 1492-1500.
[14]	LAM C S P, SOLOMON S D. Classification of HeartFailure according to ejection fraction: JACC review topic of the week[J]. J Am Coll Cardiol, 2021, 77(25): 3217-3225. doi: 10.1016/j.jacc.2021.04.070
[15]	ZAMZMI G, HSU L Y, LI W, et al. Harnessing machine intelligence in automatic echocardiogram analysis: current status, limitations, and future directions[J]. IEEE Rev Biomed Eng, 2021, 14: 181-203. doi: 10.1109/RBME.2020.2988295
[16]	OSTVIK A, SMISTAD E, AASE S A, et al. Real-time standard view classification in transthoracic echocardiography using convolutional neural networks[J]. Ultrasound Med Biol, 2019, 45(2): 374-384. doi: 10.1016/j.ultrasmedbio.2018.07.024
[17]	MORADI S, OGHLI M G, ALIZADEHASL A, et al. MFP-Unet: a novel deep learning based approach for left ventricle segmentation in echocardiography[J]. Phys Med, 2019, 67: 58-69. doi: 10.1016/j.ejmp.2019.10.001
[18]	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. doi: 10.1038/s41586-020-2145-8
[19]	REYNAUD H, VLONTZOS A, HOU B, et al. Ultrasound video transformers for cardiac ejection fraction estimation[C]//Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part Ⅵ. ACM, 2021: 495-505.
[20]	PRADA G, FRITZ A V, RESTREPO-HOLGUÍN M, et al. Focused cardiac ultrasonography for left ventricular systolic function[J]. N Engl J Med, 2019, 381(21): e36.
[21]	ASCH F M, MOR-AVI V, RUBENSON D, et al. Deep learning-based automated echocardiographic quantification of left ventricular ejection fraction: a point-of-care solution[J]. Circ Cardiovasc Imaging, 2021, 14(6): e012293. doi: 10.1161/CIRCIMAGING.120.012293

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A preliminary exploration of a deep learning-based artificial intelligence model for automatic quantification of echocardiographic left ventricular ejection fraction