In this study, multilayer perceptron artificial neural networks are used to predict orthodontic treatment plans, including the determination of extraction-nonextraction, extraction patterns, and anchorage patterns. The neural network can output the feasibilities of several applicable treatment plans, offering orthodontists flexibility in making decisions. The neural network models show an accuracy of 94.0% for extraction-nonextraction prediction, with an area under the curve (AUC) of 0.982, a sensitivity of 94.6%, and a specificity of 93.8%. The accuracies of the extraction patterns and anchorage patterns are 84.2% and 92.8%, respectively. The most important features for prediction of the neural networks are “crowding, upper arch” “ANB” and “curve of Spee”. For handling discrete input features with missing data, the average value method has a better complement performance than the k-nearest neighbors (k-NN) method; for handling continuous features with missing data, k-NN performs better than the other methods most of the time. These results indicate that the proposed method based on artificial neural networks can provide good guidance for orthodontic treatment planning for less-experienced orthodontists.

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By associative memory, people can remember a pattern in microseconds to seconds. In order to emulate human memory, an artificial neural network should also spend a reasonable time in recalling matters of different task difficulties or task familiarities. In this paper, we study the recall time in a memristive Hopfield network (MHN) implemented with memristor-based synapses. With the operating frequencies of 1-100 kHz, patterns can be stored into the network by altering the resistance of the memristors, and the pre-stored patterns can be successfully recalled, being similar to the associative memory behavior. For the same target pattern (the same familiarity), recall time of the MHN varies with the inputs, which is similar to the effect in the human brain that recall time depends on task difficulty. On the other hand, for the same input (i.e., the same initial state), the recall time may be different for different target patterns, which is similar to the effect in the brain that recall time depends on the familiarity. In addition, the effect of stimulation (updating frequency) on recall time may be complicated: a higher stimulation frequency may not always lead to a faster recall (it may even slow the recalling process in some circumstances). Our memristive Hopfield network shows good potential in mimicking the characteristics of human associative memory.INDEX TERMS Memristors, associative memory, Hopfield neural networks, neuromorphics.