Value of artificial intelligence based three-dimensional echocardiography in quantitative evaluation of right ventricular function

doi:10.3877/cma.j.issn.1672-6448.2022.12.003

Abstract

Abstract:

Objective

To evaluate the value of a new three-dimensional echocardiography (3DE) algorithm based on artificial intelligence (AI) for quantitative assessment of right ventricular (RV) function.

Methods

Fifty-one patients who underwent cardiac magnetic resonance (CMR) examination at Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology from January 2021 to February 2021 were enrolled. 3DE images were obtained within 24 hours, and AI based RV 3DE software (AI-3DE) was used to automatically analyze and measure the RV functional parameters. RV 3DE image quality was graded, and the process and time of analyzing RV function by AI software under different image quality were recorded. The RV ejection fraction (RVEF) measured by CMR (CMR-RVEF) was regarded as the gold standard. And RVEF measured by AI-3DE was compared to the CMR results. CMR-RVEF＜45% was defined as reduced RV systolic function. The differences between the quantitative parameters obtained by AI-3DE in the normal RV function and reduced RV function groups were compared, and the diagnostic performance of each parameter in identifying RV dysfunction was assessed by receiver operating characteristic (ROC) curve analysis.

Results

Multi-parameter quantitative analysis of RV function was completed in all patients by AI-3DE, of which 19 patients (37.3%) achieved fully RV automatic quantitative analysis within 15±1 s. The RV endometrium tracing needed to be adjusted manually in 32 patients (62.7%). And the analysis time was 100±12 s, 105±6 s, and 162±3 s in the patients with good, moderate, and poor image quality, respectively (F=1964.9, P＜0.05). Both in the overall population and in patients with moderate and poor image quality, the RVEF measured by AI-3DE was statistically correlated with the results of CMR analysis (r=0.735, P＜0.05). Bland-Altman curve showed that the RVEF measured by AI-3DE was slightly underestimated compared with CMR, but they had a good consistency. RV function parameters measured by AI-3DE, including RVEF, tricuspid ring systolic displacement (TAPSE), area change fraction (FAC), and longitudinal strain of septum and right ventricular free wall (LS), showed excellent diagnostic performance in identifying the reduced RV systolic function (P＜0.05). The cutoff value of RVEF measured by AI-3DE for the diagnosis of RV dysfunction was 43%, with a sensitivity of 94.4% and specificity of 66.6%.

Conclusion

The right ventricular 3DE algorithm based on AI can quickly and accurately evaluate the RV function, providing multiple quantitative parameters for the identification of RV dysfunction.

Key words: Artificial intelligence, Three-dimensional echocardiography, Right ventricle function, Right ventricle ejection fraction

Yuwei Bao, Ying Zhu, Kangchao Zheng, Wei Zhou, Yani Liu, Youbin Deng. Value of artificial intelligence based three-dimensional echocardiography in quantitative evaluation of right ventricular function[J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2022, 19(12): 1329-1335.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://chaosheng.cma-cmc.com.cn/EN/10.3877/cma.j.issn.1672-6448.2022.12.003

https://chaosheng.cma-cmc.com.cn/EN/Y2022/V19/I12/1329

Figures/Tables 6

参数	RV功能正常组（CMR RVEF≥45%，n=36）	RV功能减低组（CMR RVEF＜45%，n=15）	P 值
年龄［岁，M（P₂₅，P₇₅）］	54（35，61）	38（29，60）	0.291
性别［例（%）］	24（66.7）	5（33.3）	0.029
身高（cm， $x ¯$ ±s）	166.9±8.5	163.3±7.8	0.137
体重（kg， $x ¯$ ±s）	65.1±12.1	64.4±10.6	0.800
体表面积（m²， $x ¯$ ±s）	1.7±0.2	1.7±0.2	0.536
收缩压（mmHg， $x ¯$ ±s）	125±18	119±22	0.298
舒张压（mmHg， $x ¯$ ±s）	77±11	77±10	0.658
CMR RVEF［%，M（P₂₅，P₇₅）］	49（50，55）	40（27，43）	＜0.001
AI-3DE RVEF［%，M（P₂₅，P₇₅）］	49（45，52）	40（27，46）	＜0.001
TAPSE［mm，M（P₂₅，P₇₅）］	19.8（17.0，23.2）	15（12.5，18.6）	0.004
FAC［%，M（P₂₅，P₇₅）］	45.7（40.9，50.1）	37.5（27.3，43.7）	0.002
室间隔LS［-%，M（P₂₅，P₇₅）］	19.1（22.5，14.9）	12.5（15.7，9.1）	0.005
RV游离壁LS［-%，M（P₂₅，P₇₅）］	27.0（30.07，21.75）	22.8（26.4，14.4）	0.020

参数	RV功能正常组（CMR RVEF≥45%，n=36）	RV功能减低组（CMR RVEF＜45%，n=15）	P 值
年龄［岁，M（P₂₅，P₇₅）］	54（35，61）	38（29，60）	0.291
性别［例（%）］	24（66.7）	5（33.3）	0.029
身高（cm， $x ¯$ ±s）	166.9±8.5	163.3±7.8	0.137
体重（kg， $x ¯$ ±s）	65.1±12.1	64.4±10.6	0.800
体表面积（m²， $x ¯$ ±s）	1.7±0.2	1.7±0.2	0.536
收缩压（mmHg， $x ¯$ ±s）	125±18	119±22	0.298
舒张压（mmHg， $x ¯$ ±s）	77±11	77±10	0.658
CMR RVEF［%，M（P₂₅，P₇₅）］	49（50，55）	40（27，43）	＜0.001
AI-3DE RVEF［%，M（P₂₅，P₇₅）］	49（45，52）	40（27，46）	＜0.001
TAPSE［mm，M（P₂₅，P₇₅）］	19.8（17.0，23.2）	15（12.5，18.6）	0.004
FAC［%，M（P₂₅，P₇₅）］	45.7（40.9，50.1）	37.5（27.3，43.7）	0.002
室间隔LS［-%，M（P₂₅，P₇₅）］	19.1（22.5，14.9）	12.5（15.7，9.1）	0.005
RV游离壁LS［-%，M（P₂₅，P₇₅）］	27.0（30.07，21.75）	22.8（26.4，14.4）	0.020

参数	AUC	95%CI	P值	最佳截断值	敏感度（%）	特异度（%）
RVEF	0.854	0.728~0.970	＜0.001	43.0%	94.4	66.6
TAPSE	0.757	0.589~0.926	0.004	16.3 mm	88.9	66.0
FAC	0.779	0.627~0.930	0.002	39.6%	91.7	60.0
室间隔LS	0.751	0.582~0.920	0.005	-26.6%	69.4	53.3
RV游离壁LS	0.708	0.544~0.873	0.020	-21.6%	50.0	53.3

参数	观察者间（n=20）			观察者内（n=20）
参数	ICC	95%CI	P 值	ICC	95%CI	P 值
RVEF	0.812	0.693~0.888	＜0.001	0.814	0.695~0.889	＜0.001
TAPSE	0.824	0.608~0.926	＜0.001	0.870	0.700~0.946	＜0.001
FAC	0.875	0.792~0.927	＜0.001	0.808	0.577~0.919	＜0.001
室间隔LS	0.926	0.822~0.970	＜0.001	0.947	0.870~0.979	＜0.001
RV游离壁LS	0.824	0.711~0.896	＜0.001	0.886	0.808~0.933	＜0.001

References 20

1	Badano LP, Addetia K, Pontone G, et al. Advanced imaging of right ventricular anatomy and function [J]. Heart, 2020, 106(19): 1469-1476.
2	Mrad AK, Burri M, Cleuziou J, et al. Preoperative predictability of right ventricular failure following surgery for Ebstein's anomaly [J]. Eur J Cardiothorac Surg, 2019, 55(6): 1187-1193.
3	Bolat I. Preoperative right ventricular echocardiographic parameters predict perioperative cardiovascular complications in patients undergoing non-cardiac surgery [J]. Heart Lung Circ, 2020, 29(8): 1146-1151.
4	Chou J, Ma M, Gylys M, et al. Preexisting right ventricular dysfunction is associated with higher postoperative cardiac complications and longer hospital stay in high-risk patients undergoing nonemergent major vascular surgery [J]. J Cardiothorac Vasc Anesth, 2019, 33(5): 1279-1286.
5	Park JB, Lee SP, Lee JH, et al. Quantification of right ventricular volume and function using single-beat three-dimensional echocardiography: a validation study with cardiac magnetic resonance [J]. J Am Soc Echocardiogr, 2016, 29(5): 392-401.
6	Zhang QB, Sun JP, Gao RF, et al. Feasibility of single-beat full-volume capture real-time three-dimensional echocardiography for quantification of right ventricular volume: validation by cardiac magnetic resonance imaging [J]. Int J Cardiol, 2013, 168(4): 3991-3995.
7	Nesser HJ, Tkalec W, Patel AR, et al. Quantitation of right ventricular volumes and ejection fraction by three-dimensional echocardiography in patients: comparison with magnetic resonance imaging and radionuclide ventriculography [J]. Echocardiography, 2006, 23(8): 666-680.
8	Genovese D, Rashedi N, Weinert L, et al. Machine learning-based three-dimensional echocardiographic quantification of right ventricular size and function: validation against cardiac magnetic resonance [J]. J Am Soc Echocardiogr, 2019, 32(8): 969-977.
9	Lang RM, Badano LP, 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.
10	Nowak-Machen M, Lang T, Schilling A, et al. Regional right ventricular volume and function analysis using intraoperative 3-dimensional echocardiography-derived mesh models [J]. J Cardiothorac Vasc Anesth, 2019, 33(6): 1527-1532.
11	Muraru D, Badano LP, Nagata Y, et al. Development and prognostic validation of partition values to grade right 20 ventricular dysfunction severity using 3D echocardiography [J]. Eur Heart J Cardiovasc Imaging, 2020, 21(1): 10-21.
12	Chang SA, Lee SC, Kim EY, et al. Feasibility of single-beat full-volume capture real-time three-dimensional echocardiography and auto-contouring algorithm for quantification of left ventricular volume: validation with cardiac magnetic resonance imaging [J]. J Am Soc Echocardiogr, 2011, 24(8): 853-859.
13	Liu BY, Wu WC, Zeng QX, et al. The value of three-dimensional echocardiography in risk stratification in pulmonary arterial hypertension: a cross-sectional study [J]. Int J Cardiovasc Imaging, 2020, 36(4): 577-584.
14	Kusunose K, Haga A, Abe T, et al. Utilization of artificial intelligence in echocardiography [J]. Circ J, 2019, 83(8): 1623-1629.
15	Narang A, Mor-Avi V, Prado A, et al. Machine learning based automated dynamic quantification of left heart chamber volumes [J]. Eur Heart J Cardiovasc Imaging, 2019, 20(5): 541-549.
16	Namisaki H, Nabeshima Y, Kitano T, et al. Prognostic value of the right ventricular ejection fraction, assessed by fully automated three-dimensional echocardiography: a direct comparison of analyses using right ventricular-focused views versus apical four-chamber views [J]. J Am Soc Echocardiogr, 2021, 34(2): 117-126.
17	Smolarek D, Gruchala M, Sobiczewski W. Echocardiographic evaluation of right ventricular systolic function: The traditional and innovative approach [J]. Cardiol J, 2017, 24(5): 563-572.
18	Kuhn A, Meierhofer C, Rutz T, et al. Non-volumetric echocardiographic indices and qualitative assessment of right ventricular systolic function in Ebstein's anomaly: comparison with CMR-derived ejection fraction in 49 patients [J]. Eur Heart J Cardiovasc Imaging, 2016, 17(8): 930-935.
19	Liu S, Bose R, Ahmed A, et al. Artificial intelligence-based assessment of indices of right ventricular function [J]. J Cardiothorac Vasc Anesth, 2020, 34(10): 2698-2702.
20	Ahmad A, Ibrahim Z, Sakr G, et al. A comparison of artificial intelligence-based algorithms for the identification of patients with depressed right ventricular function from 2-dimentional echocardiography parameters and clinical features [J]. Cardiovasc Diagn Ther, 2020, 10(4): 859-868.

[1]	Meifang Zhang, Ying Tan, Qiaozhen Zhu, Xin Wen, Ying Yuan, Yue Qin, Hongbo Guo, Lingxiu Hou, Wenlan Huang, Guiyan Peng, Shengli Li. Artificial intelligence-based quality control of mid-sagittal plane ultrasound images for first trimester fetal crown-rump length [J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(09): 945-950.
[2]	Ying Li, Shaoqing Pan, Mingyan Ding, Dandan Sun, Haibo Qu, Peipei Hou, Fang Zhu. Evaluation of left ventricular systolic function and synchrony in advanced atrioventricular block patients with heart failure with preserved ejection fraction after left bundle branch area pacing by real-time three-dimensional echocardiography [J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(04): 430-436.
[3]	Feifei Yang, Xixiang Lin, Yixin Chen, Qiushuang Wang, Liwei Zhang, Xu Chen, Meiqing Zhang, Shuhua Wang, Kunlun He. Automatic detection of regional wall motion abnormalities by echocardiography based on deep learning [J]. Chinese Journal of Medical Ultrasound (Electronic Edition), 2023, 20(04): 424-429.
[4]	Wei Tang, Rongquan He, Suning Huang. Application of deep learning in imaging for diagnosis, treatment and prognosis prediction of breast cancer [J]. Chinese Journal of Breast Disease(Electronic Edition), 2023, 17(06): 323-328.
[5]	Shuaihua Fan, Wei Guo, Jun Guo. Current situation and prospect of application of decision tree algorithm based on machine learning in prognosis prediction of bloodstream infection [J]. Chinese Journal of Experimental and Clinical Infectious Diseases(Electronic Edition), 2023, 17(05): 289-293.
[6]	Hui Zhang, Min Cai, Xiangya Huang. Clinical application of digital technology and artificial intelligence in maxillary sinus floor augmentation [J]. Chinese Journal of Stomatological Research(Electronic Edition), 2023, 17(04): 244-252.
[7]	Xiaowei Xing, Yuchen Liu, Bing Zhao, Minggang Wang. A research on predictive value of convolutional neural network based on preoperative abdominal CT for recurrence of incisional hernia after surgical repair [J]. Chinese Journal of Hernia and Abdominal Wall Surgery(Electronic Edition), 2023, 17(06): 677-681.
[8]	Xiaowei Xing, Yuchen Liu, Minggang Wang. Application and prospects of artificial intelligence technology in the field of hernia and abdominal wall surgery [J]. Chinese Journal of Hernia and Abdominal Wall Surgery(Electronic Edition), 2023, 17(04): 390-393.
[9]	Bing Han, Jinyang Gu. Research and application prospect of deep learning neural network in diagnosis and treatments for liver cancer [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(05): 480-485.
[10]	Manshi Lei, Sisi Deng, Xinrong Wang, Jinbin Huang, Qing Xiang, Anni Xiong, Zhan'ao Meng. Application of artificial intelligence-assisted compression sensing technology in upper abdominal fat-suppressed T2WI sequence [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(05): 551-556.
[11]	Yunpeng Ge, Hongyuan Cui, Jinghai Song. Application of artificial intelligence in diagnosis, treatment and prognosis of primary liver cancer [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(04): 367-371.
[12]	Xiaodong Wang, Kai Wang, Zhao Ge. Research progress of computer vision in improvement of therapeutic effect of liver transplantation for liver cancer [J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2023, 12(04): 361-366.
[13]	Jingke Li, Yanchun Zhang, Jiayi Wu, Xiuyu Ren. Deep learning for diabetic retinopathy screening, classification and management [J]. Chinese Journal of Ophthalmologic Medicine(Electronic Edition), 2023, 13(04): 241-246.
[14]	Meiqi Ren, Junhong Li, Zhangqing Feng. Research progress on new hot issues of intermittent exotropia [J]. Chinese Journal of Ophthalmologic Medicine(Electronic Edition), 2023, 13(03): 162-166.
[15]	Ping Hu, Tengfeng Yan, Haizhu Zhou, Xingen Zhu. Advances in artificial intelligence be applied to early diagnose intracranial hemorrhage and hematoma segmentation [J]. Chinese Journal of Cerebrovascular Diseases(Electronic Edition), 2023, 17(04): 410-416.

Please choose a citation manager

Content to export