ArticleThe Angle orthodontist2025
Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.
Article in The Angle orthodontist, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Transverse Growth of Facial Soft Tissues: A Longitudinal Three-Dimensional Facial Growth Study.Orthodontics & craniofacial research · 2026Article
- Vertical Growth of Facial Soft Tissues: A Longitudinal Three-Dimensional Facial Growth Study.Orthodontics & craniofacial research · 2026Article
- Evaluation of automated tooth landmark localization on digital models.The Angle orthodontist · 2026Article
- Factors influencing the predictive performance of artificial intelligence for craniofacial growth.The Angle orthodontist · 2025Article
- What amount of data is required to develop artificial intelligence that can accurately predict soft tissue changes after orthognathic surgery?The Angle orthodontist · 2025Article
- Facial Proportions in Stunted and Non-Stunted Children Aged 7-72 Months: A Cross-Sectional Study in Bandung, Indonesia.Children (Basel, Switzerland) · 2025Article
- Morphogenetic Expression of Genes in Class II Division 2 Malocclusion: A Pedigree Study.Cureus · 2025Article
- Holdaway analysis exhibits the highest correlation with facial profile attractiveness among nine cephalometric analyses.American journal of translational research · 2025Article
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6 authors.
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Abstract
objectivesTo compare differences in craniofacial growth prediction results for Korean and American children according to growth prediction models developed using Korean and American longitudinal growth data. MATERIALS AND
methodsGrowth prediction models based on cephalometric landmarks were built for each population using longitudinally taken lateral cephalograms of Korean children and American children of northern European origin. The sample sizes of the serial datasets were 679 and 1257 for Korean and American children, respectively. On each cephalogram, 78 cephalometric landmarks were identified. The prediction models were based on the partial least squares method with 160 input and 154 output variables. For each group, growth was predicted by applying the prediction models developed using data from the same and different populations. The growth prediction results were compared and analyzed.
resultsThe growth prediction results obtained with the prediction model developed using data from the same population were more accurate (P < .0001). The results distinctively visualized the discrepancies in the growth prediction results if different population types were not considered.
conclusionsApplying a growth prediction model generated using data from the same population may be desirable.
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