ArticleDiagnostics (Basel, Switzerland)2023
Validation of Machine Learning Models for Craniofacial Growth Prediction.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for craniofacial and mandibular growth prediction: A systematic review.Clinical oral investigations · 2026Pooled it
- Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative review.The Saudi dental journal · 2026Review
- Machine Learning-Based Model for Predicting Short- and Long-Term Growth in Untreated Class III Malocclusion.Orthodontics & craniofacial research · 2026Article
- Present and Future of the Use of Artificial Intelligence in Orthodontics.Bioengineering (Basel, Switzerland) · 2026Review
- Assessing imputation techniques for missing data in small and multicollinear datasets: insights from craniofacial morphometry.BMC medical research methodology · 2026Article
- The Expanding Role of Artificial Intelligence in Dentistry: A Cross-Specialty Chairside Perspective.Cureus · 2025Review
- Skel-Net: automatic prediction of skeletal pattern on scanned lateral cephalograms using anatomical prior-guided deep learning network.BMC oral health · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Growth Prediction in Orthodontics: ASystematic Review of Past Methods up to Artificial Intelligence.Children (Basel, Switzerland) · 2025Review
- Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.International dental journal · 2025Article
- Convolutional Neural Network-Based Deep Learning Methods for Skeletal Growth Prediction in Dental Patients.Journal of imaging · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study identified the most accurate model for predicting longitudinal craniofacial growth in a Japanese population using statistical methods and machine learning. Longitudinal lateral cephalometric radiographs were collected from 59 children (27 boys and 32 girls) with no history of orthodontic treatment. Multiple regression analysis, least absolute shrinkage and selection operator, radial basis function network, multilayer perceptron, and gradient-boosted decision tree were used. The independent variables included 26 coordinated values of skeletal landmarks, 13 linear skeletal parameters, and 17 angular skeletal parameters in children ages 6 to 12 years. The dependent variables were the values of the 26 coordinated skeletal landmarks, 13 skeletal linear parameters, and 17 skeletal angular parameters at 13 years of age. The difference between the predicted and actual measured values was calculated using the root-mean-square error. The prediction model for craniofacial growth using the least absolute shrinkage and selection operator had the smallest average error for all values of skeletal landmarks, linear parameters, and angular parameters. The highest prediction accuracies when predicting skeletal linear and angular parameters for 13-year-olds were 97.87% and 94.45%, respectively. This model incorporates several independent variables and is useful for future orthodontic treatment because it can predict individual growth.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.