Early Detection of Myocardial Infarction in Low-Quality Echocardiography

نویسندگان

چکیده

Myocardial infarction (MI), or commonly known as heart attack, is a life-threatening health problem worldwide from which 32.4 million people suffer each year. Early diagnosis and treatment of MI are crucial to prevent further tissue damages death. The earliest most reliable sign ischemia regional wall motion abnormality (RWMA) the affected part ventricular muscle. Echocardiography can easily, inexpensively, non-invasively exhibit RWMA. In this article, we introduce three-phase approach for early detection in low-quality echocardiography: 1) segmentation entire left ventricle (LV) using state-of-the-art deep learning model, 2) analysis segmented LV by feature engineering, 3) detection. main contributions study highly accurate echocardiography, pseudo labeling ground-truth formation unannotated wall, first public echocardiographic dataset (HMC-QU)* Furthermore, outputs proposed significantly help cardiologists better assessment characteristics. has achieved 95.72% sensitivity 99.58% specificity segmentation, 85.97% sensitivity, 74.03% specificity, 86.85% precision on HMC-QU dataset. *The benchmark publicly shared at repository https://www.kaggle.com/aysendegerli/hmcqu-dataset

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2021

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2021.3059595