ArticleOrthodontics & craniofacial research2024
Factors influencing the development of artificial intelligence in orthodontics.
Article in Orthodontics & craniofacial research, 2024. 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.
What it found
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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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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
- 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
- Integration of artificial intelligence in orthodontic imaging: A bibliometric analysis of research trends and applications.Imaging science in dentistry · 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
- AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes.Dentistry journal · 2025Review
- Craniofacial growth prediction models based on cephalometric landmarks in Korean and American children.The Angle orthodontist · 2025Article
- 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
- Conversational artificial intelligence compared with orthodontic reference standards for orthodontic information: A systematic review of accuracy, readability, safety, and certainty of evidence.Journal of oral biology and craniofacial researchReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectivesSince developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. MATERIALS AND
methodsA total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors.
resultsIncreasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99.
conclusionThe study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.
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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.