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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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RAFT polymerization was used to prepare PMMA-b-PNIPAM copolymers. Two different chain transfer agents, tBDB and MCPDB, were used to mediate the sequential polymerizations. Micellar solutions and gels were prepared from the resulting copolymers in aqueous solution. When heated above T c of PNIPAM (about 31 8C), DLS revealed that PNIPAM coronas collapsed, resulting in aggregation of the original micelles. The micellar gels underwent syneresis above T c as water was expelled from the ordered gel structure, the lattice periodicity of which was determined by SANS. A large decrease in lattice spacing was observed above T c . The gel became more viscoelastic at high temperature, as revealed by shear rheometry which showed a large increase in G 00 .