AI and the cardiologist: when mind, heart and machine unite

Figures

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  Figure 1. The brain, AI, ML and DL—the relationship.35 AI, artificial intelligence; ARDA, automated retinal disease assessment; DL, deep learning; ML, machine learning.

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  Figure 2. A DL neural network using data from multiple variables to predict the visibility in a foggy situation at an airport36 in simplest of terms, the neural network accepts multiple inputs through its input layer. At each node, the data are analysed using a mathematical filter function, before being passed onto the next node. This repeats for hidden layers of ‘N’ depth, creating a feature map that summarises the presence of detected features in the input, honing and refining itself at each depth of layer before finally being passed out through the output layer. DL, deep learning.

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  Figure 3. Automatic border detection and strain analysisfrom the three standard apical views calculating regional and global longitudinal strain. Adapted from Davis et al.42 Global longitudinal strain (GLS) is a simple parameter that expresses longitudinal shortening as a percentage (change in length as a proportion to baseline length), and is a newly emerging topic which has a significant role in predicting cardiovascular outcomes.