Figure 2.

A convolutional neural network is trained by feeding in labelled data (in this case voltage time waveforms), and through repetition it identifies the patterns in the data that are associated with the data labels (in this example, heart pump strength, or ejection fraction). The network has two components, convolution layers that extract image components to create the artificial intelligence features, and the fully connected layers that comprise the model, that leads to the network output. While large data sets and robust computing are required to train networks, once trained, the computation requirements are substantially reduced, permitting smartphone application. AI, artificial intelligence; EF, ejection fraction.