Fig. 1.

This figure shows a basic explanation of the most frequently used supervised learning algorithm—convolutional neural networks—for diagnosing orthopaedic conditions with imaging. A convolutional neural network transforms the input (for example, a plain radiograph of the femur) into one or more classification outputs (fracture or unfractured). The expanded box is a snapshot of the convolutional process, in which the input radiograph is processed into a matrix of pixel values. After applying different filters developed in the training process, a single value is created in the output matrix (bottom right). This process is repeated in multiple hidden layers with different filters convolving across output matrices throughout hidden layers. Based on the connections and weights in the last hidden layer, the algorithm classifies the femur into fractured or not.