Early Detection of Myocardial Infarction in Low-Quality Echocardiography

Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening worldwide health problem from which 32.4 million of people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further heart tissue damages. However, MI detection in early stages is challenging because the symptoms are not easy to distinguish in electrocardiography findings or biochemical marker values found in the blood. Echocardiography is a noninvasive clinical tool for a more accurate early MI diagnosis, which is used to analyze the regional wall motion abnormalities. When echocardiography quality is poor, the diagnosis becomes a challenging and sometimes infeasible task even for a cardiologist. In this paper, we introduce a three-phase approach for early MI detection in low-quality echocardiography: 1) segmentation of the entire left ventricle (LV) wall of the heart using state-of-the-art deep learning model, 2) analysis of the segmented LV wall by feature engineering, and 3) early MI detection. The main contributions of this study are: highly accurate segmentation of the LV wall from low-resolution (both temporal and spatial) and noisy echocardiographic data, generating the segmentation ground-truth at pixel-level for the unannotated dataset using pseudo labeling approach, and composition of the first public echocardiographic dataset (HMC-QU) labeled by the cardiologists at the Hamad Medical Corporation Hospital in Qatar. Furthermore, the outputs of the proposed approach can significantly help cardiologists for a better assessment of the LV wall characteristics. The proposed method is evaluated in a 5-fold cross validation scheme on the HMC-QU dataset. The proposed approach has achieved an average level of 95.72% sensitivity and 99.58% specificity for the LV wall segmentation, and 85.97% sensitivity, 74.03% specificity, and 86.85% precision for MI detection.