Vega Roberto, Kwok Cherise, Rakkunedeth Hareendranathan Abhilash, Nagdev Arun, Jaremko Jacob L
Exo Imaging, Santa Clara, CA 95054, USA.
Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada.
Diagnostics (Basel). 2024 Aug 8;14(16):1719. doi: 10.3390/diagnostics14161719.
This work aims to evaluate the performance of a new artificial intelligence tool (ExoAI) to compute the left ventricular ejection fraction (LVEF) in echocardiograms of the apical and parasternal long axis (PLAX) views. We retrospectively gathered echocardiograms from 441 individual patients (70% male, age: 67.3 ± 15.3, weight: 87.7 ± 25.4, BMI: 29.5 ± 7.4) and computed the ejection fraction in each echocardiogram using the ExoAI algorithm. We compared its performance against the ejection fraction from the clinical report. ExoAI achieved a root mean squared error of 7.58% in A2C, 7.45% in A4C, and 7.29% in PLAX, and correlations of 0.79, 0.75, and 0.89, respectively. As for the detection of low EF values (EF < 50%), ExoAI achieved an accuracy of 83% in A2C, 80% in A4C, and 91% in PLAX. Our results suggest that ExoAI effectively estimates the LVEF and it is an effective tool for estimating abnormal ejection fraction values (EF < 50%). Importantly, the PLAX view allows for the estimation of the ejection fraction when it is not feasible to acquire apical views (e.g., in ICU settings where it is not possible to move the patient to obtain an apical scan).
这项工作旨在评估一种新型人工智能工具（ExoAI）在计算心尖和胸骨旁长轴（PLAX）视图的超声心动图中左心室射血分数（LVEF）方面的性能。我们回顾性收集了441例个体患者的超声心动图（70%为男性，年龄：67.3±15.3，体重：87.7±25.4，BMI：29.5±7.4），并使用ExoAI算法计算每个超声心动图中的射血分数。我们将其性能与临床报告中的射血分数进行了比较。ExoAI在A2C视图中的均方根误差为7.58%，在A4C视图中为7.45%，在PLAX视图中为7.29%，相关性分别为0.79、。至于低EF值（EF<50%）的检测，ExoAI在A2C视图中的准确率为83%，在A4C视图中为80%，在PLAX视图中为91%。我们的结果表明，ExoAI能有效估计LVEF，是估计异常射血分数值（EF<50%）的有效工具。重要的是，当获取心尖视图不可行时（例如，在重症监护病房环境中，无法移动患者以获得心尖扫描），PLAX视图可用于估计射血分数。
