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中华医学超声杂志（电子版）

中华医学超声杂志（电子版） ›› 2022, Vol. 19 ›› Issue (12) : 1329 -1335. doi: 10.3877/cma.j.issn.1672-6448.2022.12.003

心血管超声影像学

基于人工智能的三维超声心动图定量评估右心室功能的价值
包雨微1, 朱英1, 郑康超1, 周玮1, 刘娅妮1,†(), 邓又斌1   
  1. 1. 430030　武汉，华中科技大学同济医学院附属同济医院超声医学科
  • 收稿日期:2021-05-21 出版日期:2022-12-01
  • 通信作者: 刘娅妮

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

Yuwei Bao1, Ying Zhu1, Kangchao Zheng1, Wei Zhou1, Yani Liu1,†(), Youbin Deng1   

  1. 1. Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
  • Received:2021-05-21 Published:2022-12-01
  • Corresponding author: Yani Liu
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包雨微, 朱英, 郑康超, 周玮, 刘娅妮, 邓又斌. 基于人工智能的三维超声心动图定量评估右心室功能的价值[J]. 中华医学超声杂志（电子版）, 2022, 19(12): 1329-1335.

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.

目的

探讨基于人工智能（AI）的三维超声心动图（3DE）算法在定量评估右心室（RV）功能中的价值。

方法

连续性收集自2021年1月至2021年2月在华中科技大学同济医学院附属同济医院就诊且行心脏磁共振（CMR）的患者51例，CMR检查后24 h内进行3DE检查，使用基于AI的3DE软件自动分析右心室（RV）功能参数，记录每位患者所有参数值及分析时间。对RV 3DE图像质量进行分级评估（分为优、中等、差）。以CMR测量的RVEF（CMR-RVEF）为金标准，比较AI-3DE计算的RVEF与CMR结果的相关性及一致性；以CMR-RVEF＜45%为RV收缩功能减低，比较AI-3DE所获取的定量参数在RV功能正常与功能减低组间的差异，并绘制ROC曲线分析各参数对RV收缩功能异常的诊断价值。

结果

51例患者均应用AI-3DE完成了RV功能的多参数定量分析，19例（19/51，37.3%）未经手动校正的患者，全自动分析平均时间为（15±1）s；32例需要手动校正的患者中，AI-3DE RV分析时间在图像质量优、中等、差的患者中分别为（100±12）、（105±6）、（162±3）s，三者比较差异有统计学意义（F=1954.9，P＜0.05）。CMR与AI-3DE测量的RVEF分别为51%（44%，54%）和47%（43%，50%），2种方法的测值差异无统计学意义（P＞0.05）。相关性分析散点图显示，2种方法测量结果呈一定程度的正相关关系（r=0.735，P＜0.05）。Bland-Altman图表明，2种方法获得的差异分布在平均值附近，二者具有良好的一致性。基于AI-3DE的RVEF、三尖瓣环收缩期位移（TAPSE）、右心室面积变化分数（FAC）、室间隔长轴应变（LS）和RV游离壁LS在RV功能正常组与功能减低组之间差异均有统计学意义（P均＜0.05）。ROC曲线显示，基于AI-3DE的RVEF诊断RV功能减低的ROC曲线下面积为0.854，最佳截断值为43%，敏感度为94.4%，特异度为66.6%。

结论

基于AI的3DE算法能够快速、准确地获得反映RV功能的多参数指标，这些定量参数尤其是RVEF对RV功能减低具有良好的诊断价值。

关键词: 人工智能, 三维超声心动图, 右心室功能, 右心室射血分数
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
图1 基于人工智能的三维超声心动图（AI-3DE）多参数定量分析右心室功能。图a为右心室非容积功能参数测量；图b为右心室容积参数及右心室射血分数（RVEF）测量
图2 基于人工智能的三维超声心动图与心脏磁共振测量的右心室射血分数相关性分析散点图注：CMR为心脏磁共振；AI-3DE为基于人工智能的三维超声心动图；RVEF为右心室射血分数
图3 基于人工智能的三维超声心动图与心脏磁共振测量的右心室射血分数的Bland-Altman分析图。图中红色虚线表示平均偏差值，上下两端的黑色虚线表示95%的一致性界限，中间的黑色虚线表示差值为0的水平。如图所示，多数心脏磁共振和三维超声心动图的差异值都在95%一致性界限内注：3DE为三维超声心动图；CMR为心脏磁共振；RVEF为右心室射血分数；RVEF平均值为（3DE测值+CMR测值）/2；RVEF差值为3DE测值-CMR测值
表1 RV功能正常组与功能减低组一般资料及AI-3DE测量参数的比较
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
表2 基于AI-3DE的RV功能参数诊断RV功能减低的ROC曲线分析
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
表3 AI-3DE测量RV功能参数的观察者间和观察者内一致性
参数 观察者间（n=20） 观察者内（n=20）
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
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