Myocardial infarction (MI) is the leading cause of mortality in the world that occurs due to a blockage of the coronary arteries feeding the myocardium. An early diagnosis of MI and its localization can mitigate the extent of myocardial damage by facilitating early therapeutic interventions. Following the blockage of a coronary artery, the regional wall motion abnormality (RWMA) of the ischemic myocardial segments is the earliest change to set in. Echocardiography is the fundamental tool to assess any RWMA. Assessing the motion of the left ventricle (LV) wall only from a single echocardiography view may lead to missing the diagnosis of MI as the RWMA may not be visible on that specific view. Therefore, in this study, we propose to fuse apical 4-chamber (A4C) and apical 2-chamber (A2C) views in which a total of 12 myocardial segments can be analyzed for MI detection. The proposed method first estimates the motion of the LV wall by Active Polynomials (APs), which extract and track the endocardial boundary to compute myocardial segment displacements. The features are extracted from the A4C and A2C view displacements, which are concatenated and fed into the classifiers to detect MI. The main contributions of this study are 1) creation of a new benchmark dataset by including both A4C and A2C views in a total of 260 echocardiography recordings, which is publicly shared with the research community, 2) improving the performance of the prior work of threshold-based APs by a Machine Learning based approach, and 3) a pioneer MI detection approach via multi-view echocardiography by fusing the information of A4C and A2C views. Experimental results show that the proposed method achieves 90.91% sensitivity and 86.36% precision for MI detection over multi-view echocardiography. The software implementation is shared at https://github.com/degerliaysen/MultiEchoAI.

翻译：心肌梗死（MI）是全球首要致死病因，由供血心肌的冠状动脉阻塞引发。早期诊断及定位MI可通过促进早期治疗干预减轻心肌损伤程度。冠状动脉阻塞后，缺血心肌节段的局部室壁运动异常（RWMA）是最早出现的病理改变。超声心动图是评估RWMA的基础工具。仅从单一超声心动图视角评估左心室（LV）室壁运动可能导致MI漏诊，因为该特定视角可能无法显示RWMA。因此，本研究提出融合心尖四腔（A4C）与心尖两腔（A2C）视角，通过分析共计12个心肌节段实现MI检测。该方法首先采用主动多项式（APs）估计LV室壁运动，通过提取并追踪心内膜边界计算心肌节段位移。从A4C与A2C视角位移中提取特征，经拼接后输入分类器进行MI检测。本研究的主要贡献包括：1）创建包含A4C与A2C双视角共计260条超声心动图记录的新基准数据集，并公开供研究社区使用；2）通过基于机器学习的方法改进先前基于阈值的APs工作性能；3）首次提出通过融合A4C与A2C视角信息实现基于多视角超声心动图的MI检测方法。实验结果表明，本方法在多视角超声心动图MI检测中达到90.91%的敏感性与86.36%的精确率。软件实现代码已共享于https://github.com/degerliaysen/MultiEchoAI。