Clinical Applications of Artificial Intelligence in Vascular Surgery

Figures

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  Fig. 1. Perceptron and biological neuron. A perceptron is a fundamental building block of artificial neural networks, serving as a simple model of neurons in the brain. It processes inputs, applies a weighted sum, and produces an output based on an activation function. (A) Perceptron with two input signals. The perceptron takes inputs typically presented by x1 or x2. (B) Perceptron with input signals, output signal, and bias. Each input is assigned a weight (w1, w2) that determines its importance in the decision-making process. The weighted sum of the inputs is calculated. Bias (b) is an additional parameter that allows the model to fit data more flexibly. (C) Single-layer perceptron with an activation function. The node calculates the weighted sum of the inputs and bias. Finally, the perceptron uses an activation function, typically represented as h(), to determine whether the output signal is flowing (y). (D) Multilayer perceptrons with two hidden layers. (E) Biological neurons. The perceptron is a mathematical model of human neurons. x, input signal; y, output signal; w, weight; b, bias; a, node; h(), activation function.

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  Fig. 2. Various types of smart wearable devices.