Application of Machine Learning in Orthodontics: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Machine learning (ML), a facet of artificial intelligence, utilizes algorithms to learn from data without explicit programming. In orthodontics, ML offers advantages like tailoring personalized treatment plans for patients. Despite its potential, there hasn’t been a bibliometric analysis of ML studies in orthodontics. This study aims to fill that gap. Types of studies reviewed: Articles on ML in orthodontics were reviewed from Web of Science Core Collection, Embase, Scopus, and PubMed. Data on journal details, country of origin, publication month, citations, keywords, and co-authorship were extracted. Results: The search retrieved a total of 1478 articles, of which 701 were excluded. American Journal of Orthodontics and Dentofacial Orthopedics has published the most articles (3.6%), followed by the seminars in Orthodontics Journal (1.6%), and Orthodontics and Craniofacial Research Journal (1.6%). Most of the articles were from researchers from China (n = 156), the United States (n = 107), and South Korea (n = 70). The number of citations of the published articles ranged from 0 to 702, with most articles (75.54%) having at least one citation. Science Mapping analysis revealed that the most used keywords were Human(s) (n = 484), Artificial intelligence (n = 194), Female (n=169), Male (n = 161), and Cephalometry (n = 151). Clinical implications: Clinicians should be aware of the emerging global collaborative landscape in machine learning trends, stay informed about technological advancements, and consider the potential impact of ML on patient care and treatment outcomes in their practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.202 | 0.211 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".