Batool Samana, Taj Imtiaz Ahmad, Ghafoor Mubeen
Electrical Engineering, Capital University of Science and Technology, Islamabad Expressway, Kahuta Road, Islamabad 44000, Pakistan.
School of Computer Science, University of Lincoln, Brayford Way, Brayford, Pool, Lincoln LN6 7TS, UK.
Diagnostics (Basel). 2023 Jun 24;13(13):2155. doi: 10.3390/diagnostics13132155.
Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson's method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.
超声心动图是最常用于评估心脏解剖结构和功能的成像系统之一。左心室射血分数（LVEF）是通过测量左心室（LV）参数从超声心动图评估的一个重要临床变量。当心脏病专家使用大量超声心动图数据对LVEF进行量化时，会出现显著的观察者间和观察者内变异性。机器学习算法有能力分析如此庞大的数据集，并识别高技能观察者可能忽略的心脏结构和功能的复杂模式，从而为该领域的计算机辅助诊断铺平道路。在本研究中，对超声心动图数据进行左心室分割，然后基于临床方法从左心室提取特征。然后使用神经网络和传统机器学习算法对提取的特征进行分析，以估计LVEF。结果表明，对从左心室提取的特征采用机器学习技术比使用辛普森法估计LVEF具有更高的准确性。评估是在一个公开可用的超声心动图数据集EchoNet-Dynamic上进行的。当使用卷积神经网络架构DeepLab进行左心室分割，并使用长短期记忆网络（LSTM）进行LVEF回归时，可获得最佳结果，骰子相似系数为0.92，平均绝对误差为5.736%。
