Echocardiography AI

Automated echocardiographic interpretation hinges on the correct recognition of the view (imaging plane and orientation). Current state-of-the-art methods for identifying the view computationally involve 2-dimensional convolutional neural networks (CNNs) and ignore information describing the movement of structures throughout the cardiac cycle. Here we explore the efficacy of novel CNN architectures and find that these can more than halve the error rate of traditional CNNs. The work is published inĀ JMAI.