Detection of Hypertrophic Cardiomyopathy from Echocardiography: A Survey of Current Approaches

Hypertrophic cardiomyopathy (HCM) is a genetic disorder characterized by the thickening of the heart walls, which can lead to heart failure and other complications. Echocardiography is a non-invasive diagnostic tool that can be used to detect HCM. In this study, we survey the existing approaches for detection of HCM from echocardiography. We reviewed the literature on various techniques used to analyze echocardiogram videos and images, including traditional image processing methods, machine learning techniques, and deep learning methods. We also discussed the advantages and disadvantages of each approach, as well as the challenges that need to be addressed in the future. The results of this study show that deep learning methods, particularly those based on convolutional neural networks (CNNs), have shown promise for the detection of HCM from echocardiography. However, further research is needed to improve the performance of these methods and to make them more widely applicable in clinical practice. Overall, this study provides a comprehensive overview of the current state of the art in HCM detection from echocardiography and highlights the areas that require further research.