ArticleThe Angle orthodontist2024
Comparison of individualized facial growth prediction models based on the partial least squares and artificial intelligence.
Article in The Angle orthodontist, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Artificial intelligence for craniofacial and mandibular growth prediction: A systematic review.Clinical oral investigations · 2026Pooled it
- Evaluation of automated tooth landmark localization on digital models.The Angle orthodontist · 2026Article
- Current Applications and Future Perspectives of Artificial Intelligence in Face-Driven Orthodontics: A Scoping Review.Biomimetics (Basel, Switzerland) · 2026Review
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- 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
- 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
- Artificial Intelligence in Dentistry: A Narrative Review of Diagnostic and Therapeutic Applications.Medical science monitor : international medical journal of experimental and clinical research · 2025Review
- Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.The Angle orthodontist · 2025Article
- Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs.International dental journal · 2025Article
- AI-driven prediction of treatment outcome in aligner therapy: A pilot study.Bioinformation · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare facial growth prediction models based on the partial least squares and artificial intelligence (AI). MATERIALS AND
methodsSerial longitudinal lateral cephalograms from 410 patients who had not undergone orthodontic treatment but had taken serial cephalograms were collected from January 2002 to December 2022. On every image, 46 skeletal and 32 soft-tissue landmarks were identified manually. Growth prediction models were constructed using multivariate partial least squares regression (PLS) and a deep learning method based on the TabNet deep neural network incorporating 161 predictor, and 156 response, variables. The prediction accuracy between the two methods was compared.
resultsOn average, AI showed less prediction error by 2.11 mm than PLS. Among the 78 landmarks, AI was more accurate in 63 landmarks, whereas PLS was more accurate in nine landmarks, including cranial base landmarks. The remaining six landmarks showed no statistical difference between the two methods. Overall, soft-tissue landmarks, landmarks in the mandible, and growth in the vertical direction showed greater prediction errors than hard-tissue landmarks, landmarks in the maxilla, and growth changes in the horizontal direction, respectively.
conclusionsPLS and AI methods seemed to be valuable tools for predicting growth. PLS accurately predicted landmarks with low variability in the cranial base. In general, however, AI outperformed, particularly for those landmarks in the maxilla and mandible. Applying AI for growth prediction might be more advantageous when uncertainty is considerable.
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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.