Figure 2 |. Development of machine learning algorithms and their architecture and functionality.

(a) The first neural networks used in image analysis were the simple perceptrons with a single input layer connected to a binary output. (b) Radial basis forward networks represent an intermediate stage with a hidden layer in between the input and output. (c) The current state of the art in the analysis of whole slide images is the convolutional neural network (CNN) that links the pixel input to the classification output via several interconnected hidden layers as shown in this fictional example for the classification of severity of T cell–mediated rejection (TCMR), antibody-mediated rejection (AMR), and scar in a rodent kidney allograft. Note that the hidden layers in such CNNs currently remain a black box. However, the trust-building mandatory transparency can be achieved with additional software, as shown in Figure 3. To optimize viewing of this image, please see the online version of this article at www.kidney-international.org.