Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.

Abstract

Background: Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of interdisciplinary approach, we aim to predict the changes of incisor and profile, while identifying significant predictors.

Methods: A three-layer back-propagation artificial neural network model (BP-ANN) was constructed to predict incisor and profile changes of 346 patients, they were randomly divided into training, validation and testing cohort in the ratio of 7:1.5:1.5. The input data comprised of 28 predictors (model measurements, cephalometric analysis and other relevant information). Changes of U1-SN, LI-MP, Z angle and facial convex angle were set as continuous outcomes, mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R²) were used as evaluation index. Change trends of Z angle and facial convex angle were set as categorical outcomes, accuracy, precision, recall, and F1 score were used as evaluation index. Furthermore, we utilized SHapley Additive exPlanations (SHAP) method to identify significant predictors in each model.

Results: MSE/MAE/R² values for U1-SN were 0.0042/0.055/0.84, U1-SN, MP-SN and ANB were identified as the top three influential predictors. MSE/MAE/R² values for L1-MP were 0.0062/0.063/0.84, L1-MP, ANB and extraction pattern were identified as the top three influential predictors. MSE/MAE/R² values for Z angle were 0.0027/0.043/0.80, Z angle, MP-SN and LL to E-plane were considered as the top three influential indicators. MSE/MAE/R² values for facial convex angle were 0.0042/0.050/0.73, LL to E-plane, UL to E-plane and Z angle were considered as the top three influential indicators. Accuracy/precision/recall/F1 Score of the change trend of Z angle were 0.89/1.0/0.80/0.89, Z angle, Lip incompetence and LL to E-plane made the largest contributions. Accuracy/precision/recall/F1 Score of the change trend of facial convex angel were 0.93/0.87/0.93/0.86, key contributors were LL to E-plane, UL to E-plane and Z angle.

Conclusion: BP-ANN could be a promising method for objectively predicting incisor and profile changes prior to orthodontic treatment. Such model combined with key influential predictors could provide valuable reference for decision-making process and personalized aesthetic predictions.