ArticleHead & face medicine2025
Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach.
Article in Head & face medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Final incisor position across sagittal and vertical skeletal patterns in Chinese orthodontic patients with acceptable posttreatment profiles.Clinical oral investigations · 2026Article
- Predicting gingival embrasure risk after invisible orthodontics using multimodal data and machine learning.Acta odontologica Scandinavica · 2026Article
- Correlation Between Lip Prominence and Orthodontic Incisor Repositioning Within an Aesthetic Triangle Framework.Medicina (Kaunas, Lithuania) · 2026Article
- Three-dimensional dynamic evaluation of facial soft tissue changes following anterior traction in growing Angle Class III malocclusion patients.Progress in orthodontics · 2026Article
- Present and Future of the Use of Artificial Intelligence in Orthodontics.Bioengineering (Basel, Switzerland) · 2026Review
- Assessment of lower incisor position and symphysis dimensions among different skeletal patterns in the Chhattisgarh population.Bioinformation · 2026Article
- Orthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundFacial 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.
methodsA 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.
resultsMSE/MAE/R
conclusionBP-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.
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Registered trials
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