Application of artificial intelligence in trauma orthopedics: Limitation and prospects

Figure 1 Diagram demonstration of convolutional neural networks in the artificial intelligence hierarchy.

Figure 2 Schematic exemplification of an artificial neuron to highlight its similarity to a biological neuron. Data input (X) is termed weight (W) in an artificial neuron. As in, Xs are multiplied by their Ws, bias value (b) is added to allow the model to fit better, and a nonlinear mathematical formula determines the output (Y) for the next neurons in line.

Figure 3 Schematic representation illustrated an artificial network inspired by a biological neural system. A: Graphic model of artificial neural network and its similarity to (B) biological neural network; B: Biological neural network. Output of one layer is considered the input of another.

Figure 4 Schematic representation of detection of fracture and localizing it. A: To better outline the fractures, 10 convolutional neural networks were used to predict and generate bounding boxes around them; B: Sixteen anatomical regions and the result of fracture detection in them. Reprinted with permission from Jones et al[28].