Diagnostic accuracy of artificial intelligence in determining extraction protocol in orthodontic patients: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To assess the diagnostic accuracy of artificial intelligence-based models in the determination of tooth extraction in orthodontic treatment planning. MATERIALS AND METHODS: A comprehensive literature search was conducted in multiple databases (PubMed, LILACS, Web of Science, Scopus, EBSCO, and Google Scholar) up to June, 2024. Studies that met the inclusion criteria based on the PIRD (Participants, Index test, Reference test, Diagnostic) framework were selected. The risk of bias of included studies was assessed using the QUADAS-2 tool, and their methodological quality was evaluated as well using a standardized checklist. RESULTS: Out of 361 retrieved records, eleven studies were included in this review. Nine of these studies achieved a score of over 50% on the AI quality checklist, indicating acceptable methodological quality. However, a comprehensive assessment using the QUADAS-2 tool revealed that all studies had some level of risk of bias, particularly in patient selection, the conduct of AI-based predictions, and the reference standard used. CONCLUSION: Neural networks and classifier models demonstrated the high level of accuracy ranging from 82% to 94% in determining the optimal tooth extraction protocol. However, to ensure reliable predictions, artificial intelligence-based models should be rigorously trained, incorporating a comprehensive range of factors.
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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 | 0.045 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".