A Review of Deep Learning Approaches with CMR Images in the Diagnosis of Cardiovascular Diseases
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Abstract
Cardiovascular disease (CVD) is one of the leading causes of death worldwide, which has led to the recent intensification of Deep Learning (DL) studies in the field of cardiology. Patients usually experience symptoms such as rapid fatigue, edema below the knee and ankle, chest pain, shortness of breath, and palpitations. The most common types of CVD include coronary artery disease, arrhythmias, congenital heart defects, cardiomyopathy, valvular heart failure, and angina. Electrocardiography (ECG), blood tests, physical examination, and medical imaging are the most effective tools for diagnosing diseases. In recent years, cardiac magnetic resonance imaging (CMRI) has been increasingly used for the diagnosis, follow-up, treatment planning, and prognosis of CVDs. However, the large number of slices and low contrast of CMRI data make diagnosing CVD difficult. Deep learning techniques are being applied to diagnose CVD with CMRI data to overcome these difficulties, and intensive research continues to be conducted in this field. It is important to keep abreast of developments so that these studies can significantly impact clinical applications. This review aims to be a stepping stone for researchers in this process by comprehensively reviewing studies on CVD detection using DL methods on CMRI images.
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References
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