Deep learning vs traditional methods for echocardiogram analysis on heart disease

Heart disease remains a leading cause of mortality worldwide, necessitating accurate and efficient diagnostic tools. Echocardiograms are a primary imaging modality for assessing cardiac function, but their manual interpretation is time-consuming and subject to variability. Recent advancements in deep learning (DL) have shown promise in automating echocardiogram analysis, potentially outperforming traditional image processing and machine learning methods. This study compares the effectiveness of deep learning techniques, such as convolutional neural networks (CNNs) and recurrent architectures, against conventional approaches like speckle tracking, Doppler analysis, and feature-based machine learning in detecting and diagnosing heart disease from echocardiographic data. By referencing to other similar result that uses different type of methods we can see the improvement of a higher accuracy in detection rate in heart disease with deep learning.