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Realtime Automatic Assessment of Cardiac Function in Echocardiography

机译:实时自动评估超声心动图功能

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摘要

Assessment of cardiac function by echocardiography is challenging for nonexperts. In a patient with dyspnea, quantification of the mitral annular excursion (MAE) and velocities is important for the diagnosis of heart failure. The displacement of the atrioventricular (AV) plane is a good indicator of systolic left ventricular function, while the peak velocities give supplementary information about the systolic and diastolic function. By measuring these parameters automatically, a preliminary diagnosis can be given by the nonexpert. We propose an automatic algorithm to localize the mitral annular points in an apical four-chamber view and estimate the MAE, as well as the systolic, early diastolic, and late diastolic tissue peak velocities, by using a deformable ventricle model for orientation and tissue Doppler data for tracking. Automatic parameter estimates from 367 tissue Doppler recordings were compared to reference measurements by experienced cardiologists to assess the accuracy of the estimation, as well as the ability to correctly detect reduced MAE, which we defined as less than 10 mm. The dataset consisted of 200 recordings from a patient population and 167 healthy from a population study. When considering the average of the septal and lateral values, the estimation error for the MAE had a standard deviation of 2.1 mm, which was reduced to 1.9 mm when excluding recordings for which the automatic segmentation failed to locate the AV plane (41 recordings). The corresponding standard deviations for the peak velocities were around 1 cm/s. The classification of MAE was correct in 90% of the cases and had a sensitivity of 83% and a specificity of 92%. We conclude that the algorithm has good accuracy and note that the estimation error for the MAE was comparable to interobserver and methodology agreements reported in the literature.
机译:对于非专家而言,通过超声心动图评估心脏功能具有挑战性。对于呼吸困难的患者,二尖瓣环偏移(MAE)和速度的量化对于心力衰竭的诊断很重要。房室(AV)平面的位移是收缩期左心室功能的良好指标,而峰值速度可提供有关收缩期和舒张功能的补充信息。通过自动测量这些参数,非专家可以提供初步诊断。我们提出了一种自动算法,可在心尖四腔视图中定位二尖瓣环点,并通过使用可变形的心室模型来确定方向和组织多普勒,估算MAE以及收缩期,舒张早期和舒张晚期组织的峰值速度跟踪数据。由经验丰富的心脏病专家将367个组织多普勒记录的自动参数估计值与参考测量值进行比较,以评估估计值的准确性以及正确检测降低的MAE的能力,我们将其定义为小于10毫米。数据集包括来自患者人群的200条记录和来自人群研究的167条健康记录。当考虑间隔和侧面值的平均值时,MAE的估计误差的标准偏差为2.1mm,当排除自动分割无法定位AV平面的记录(41条记录)时,其标准偏差减小至1.9mm。峰值速度的相应标准偏差约为1 cm / s。在90%的病例中,MAE的分类是正确的,敏感性为83%,特异性为92%。我们得出结论,该算法具有良好的准确性,并注意到MAE的估计误差与文献中报道的观察者间和方法学上的协议可比。

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