Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta analysis.

Abstract

Background: This study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI) models in predicting dental extractions during orthodontic treatment planning.

Method: A 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.

Results: Seven 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.

Conclusion: AI 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.