ArticleThe Angle orthodontist2022
Evaluation of an individualized facial growth prediction model based on the multivariate partial least squares method.
Article in The Angle orthodontist, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Artificial intelligence for craniofacial and mandibular growth prediction: A systematic review.Clinical oral investigations · 2026Pooled it
- Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.BMC oral health · 2025Article
- Factors influencing the predictive performance of artificial intelligence for craniofacial growth.The Angle orthodontist · 2025Article
- Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.Children (Basel, Switzerland) · 2025Review
- Comparison of individualized facial growth prediction models using artificial intelligence and partial least squares based on the Mathews growth collection.The Angle orthodontist · 2025Article
- Morphogenetic Expression of Genes in Class II Division 2 Malocclusion: A Pedigree Study.Cureus · 2025Article
- Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.The Angle orthodontist · 2025Article
- A New Method of Predicting Final Mandibular Length Based on the Morphology of Cervical Vertebrae.Diagnostics (Basel, Switzerland) · 2024Article
- Factors influencing the development of artificial intelligence in orthodontics.Orthodontics & craniofacial research · 2024Article
- Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.The Angle orthodontist · 2024Article
- Orthodontic treatment outcome predictive performance differences between artificial intelligence and conventional methods.The Angle orthodontist · 2024Article
- Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?The Angle orthodontist · 2024Article
- Accuracy of Artificial Intelligence in Predicting Facial Changes Post-Orthognathic Surgery: A Comprehensive Scoping Review.Journal of clinical and experimental dentistry · 2024Article
- Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.The Angle orthodontist · 2024Article
- Validation of Machine Learning Models for Craniofacial Growth Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review.Journal of Zhejiang University. Science. B · 2023Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesTo develop a facial growth prediction model incorporating individual skeletal and soft tissue characteristics. MATERIALS AND
methodsSerial longitudinal lateral cephalograms were collected from 303 children (166 girls and 137 boys), who had never undergone orthodontic treatment. A growth prediction model was devised by applying the multivariate partial least squares (PLS) algorithm, with 161 predictor variables. Response variables comprised 78 lateral cephalogram landmarks. Multiple linear regression analysis was performed to investigate factors influencing growth prediction errors.
resultsUsing the leave-one-out cross-validation method, a PLS model with 30 components was developed. Younger age at prediction resulted in greater prediction error (0.03 mm/y). Further, prediction error increased in proportion to the growth prediction interval (0.24 mm/y). Girls, subjects with Class II malocclusion, growth in the vertical direction, skeletal landmarks, and landmarks on the maxilla were associated with more accurate prediction results than boys, subjects with Class I or III malocclusion, growth in the anteroposterior direction, soft tissue landmarks, and landmarks on the mandible, respectively.
conclusionsThe prediction error of the prediction model was proportional to the remaining growth potential. PLS growth prediction seems to be a versatile approach that can incorporate large numbers of predictor variables to predict numerous landmarks for an individual subject.
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