Malocclusion is a common disease that impairs occlusal function, increases the incidence of caries, causes psychological discomfort, endangers health and reduces the quality of life 1, 2, 3. These results indicate that the proposed method based on artificial neural networks can provide good guidance for orthodontic treatment planning for less-experienced orthodontists. 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. The most important features for prediction of the neural networks are “crowding, upper arch” “ANB” and “curve of Spee”. The accuracies of the extraction patterns and anchorage patterns are 84.2% and 92.8%, respectively.
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 neural network can output the feasibilities of several applicable treatment plans, offering orthodontists flexibility in making decisions. 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.
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