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

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