SynthesisBMC oral health2025
Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta analysis.
Synthesis in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Beyond the algorithm potential: orthodontic tooth-extraction decisions in the age of AI.Evidence-based dentistry · 2026Pooled it
- Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative review.The Saudi dental journal · 2026Review
- Review
- Multilevel Determinants of Engagement in Lifelong Learning in Orthodontics From Formal to AI-Supported Approaches: Development of a Conceptual Framework Through a Qualitative Study.JMIR medical education · 2026Article
- Artificial neural network in orthodontic therapeutic extractions.Bioinformation · 2026Article
- A dialectical lens for AI and medical humanities: advancing responsible augmented humanism in Digital Public Health.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) models in predicting dental extractions during orthodontic treatment planning.
methodA systematic review and meta-analysis were conducted following PRISMA guidelines and registered in PROSPERO (CRD42024582455). Comprehensive searches were performed across PubMed, Scopus, Web Of Science, and Google Scholar up to June 2, 2025. Eligible cross-sectional studies assessing AI-based models against clinical standards were included. Data on model performance were extracted and pooled using a random-effects model. Subgroup and meta-regression analyses were conducted to explore heterogeneity.
resultsSeven cross-sectional studies from six countries with a combined sample of 6,261 patients were included. Pooled sensitivity and specificity of AI models were 70% (95% CI: 61-78) and 90% (95% CI: 87-92), respectively, though heterogeneity was high (I² = 96.7% and 93.7%). Convolutional neural networks (CNN)-based models (ResNet and VGG) demonstrated the highest diagnostic performance with no heterogeneity. Meta-regression showed that disease prevalence significantly influenced sensitivity (p = 0.050). Funnel plots revealed asymmetry, suggesting possible publication bias.
conclusionAI models, particularly CNN-based models, show promising accuracy in predicting the need for orthodontic extractions. Therefore, they can be used to create predictive models for orthodontic extractions to increase accuracy. Due to the high heterogeneity, further large-scale studies are needed to support clinical implementation.
Indexed as
Identifiers
What OpenQuestion holds
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.