Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh Campus, Sungai Buloh, Malaysia.
Cardiac Vascular and Lung Research Institute, Universiti Teknologi MARA (UiTM), Shah Alam, Malaysia.
PLoS One. 2024 Sep 12;19(9):e0310107. doi: 10.1371/journal.pone.0310107. eCollection 2024.
Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echocardiography analyses typically concentrate on peak values from left ventricular (LV) displacement curves, based on LV contour annotations or key frames during the heart's systolic or diastolic phases within a single echocardiographic cycle. This approach may overlook the rich motion field features available in multi-cycle cardiac data, which could enhance RWMA detection.
In this research, we put forward an innovative approach to detect RWMA by harnessing motion information across multiple echocardiographic cycles and multi-views. Our methodology synergizes U-Net-based segmentation with optical flow algorithms for detailed cardiac structure delineation, and Temporal Convolutional Networks (ConvNet) to extract nuanced motion features. We utilize a variety of machine learning and deep learning classifiers on both A2C and A4C views echocardiograms to enhance detection accuracy. A three-phase algorithm-originating from the HMC-QU dataset-incorporates U-Net for segmentation, followed by optical flow for cardiac wall motion field features. Temporal ConvNet, inspired by the Temporal Segment Network (TSN), is then applied to interpret these motion field features, independent of traditional cardiac parameter curves or specific key phase frame inputs.
Employing five-fold cross-validation, our SVM classifier demonstrated high performance, with a sensitivity of 93.13%, specificity of 83.61%, precision of 88.52%, and an F1 score of 90.39%. When compared with other studies using the HMC-QU datasets, these Fig s stand out, underlining our method's effectiveness. The classifier also attained an overall accuracy of 89.25% and Area Under the Curve (AUC) of 95%, reinforcing its potential for reliable RWMA detection in echocardiographic analysis.
This research not only demonstrates a novel technique but also contributes a more comprehensive and precise tool for early myocardial infarction diagnosis.
区域性壁运动异常（RWMA）是心肌梗死（MI）的早期指标，MI 是全球死亡率最高的疾病。准确和早期检测 RWMA 对于 MI 的成功治疗至关重要。目前，自动化超声心动图分析通常集中于左心室（LV）位移曲线的峰值，基于 LV 轮廓注释或单个心动周期内收缩或舒张阶段的关键帧。这种方法可能会忽略多周期心脏数据中可用的丰富运动场特征，这些特征可以增强 RWMA 检测。
在这项研究中，我们提出了一种利用多个心动周期和多视图的运动信息来检测 RWMA 的创新方法。我们的方法将基于 U-Net 的分割与光流算法相结合，用于详细的心脏结构描绘，并使用时间卷积网络（ConvNet）提取细微的运动特征。我们在 A2C 和 A4C 视图超声心动图上使用各种机器学习和深度学习分类器来提高检测准确性。一个源于 HMC-QU 数据集的三阶段算法，结合 U-Net 进行分割，然后是光流进行心脏壁运动场特征。然后，受时间分段网络（TSN）启发的时间卷积网络（Temporal ConvNet）被应用于解释这些运动场特征，而无需传统的心脏参数曲线或特定的关键相位帧输入。
使用五折交叉验证，我们的 SVM 分类器表现出了很高的性能，灵敏度为 93.13%，特异性为 83.61%，精度为 88.52%，F1 得分为 90.39%。与使用 HMC-QU 数据集的其他研究相比，这些结果突出了我们方法的有效性。该分类器还实现了 89.25%的总体准确性和 95%的曲线下面积（AUC），这表明它在超声心动图分析中具有可靠的 RWMA 检测潜力。
这项研究不仅展示了一种新的技术，还为早期心肌梗死诊断提供了更全面和精确的工具。
