Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta analysis
AI Models Demonstrate Promising 70% Sensitivity and 90% Specificity in Orthodontic Extraction Prediction
A systematic review and meta-analysis of seven studies, encompassing 6,261 patients, reveals that AI models, particularly CNN-based ones, offer moderate to high diagnostic accuracy in predicting the need for orthodontic extractions. Despite high heterogeneity, these models represent a significant advancement in dental treatment planning, suggesting potential for increased accuracy and standardization.
Driving Precision in Orthodontics with AI
AI's diagnostic capabilities are transforming orthodontic treatment planning, offering significant improvements in accuracy and efficiency for dental professionals.
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Pooled Sensitivity
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Pooled Specificity
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Studies Included
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Total Patients Analyzed
Deep Analysis & Enterprise Applications
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70%
Pooled Sensitivity
AI models achieved a pooled sensitivity of 70%, indicating a moderate to high ability to correctly identify cases requiring extractions. This finding suggests a significant potential for AI in improving diagnostic accuracy in orthodontic treatment planning, although individual model performance can vary.
| Model Type | Sensitivity (95% CI) | Specificity (95% CI) | Heterogeneity (I²) |
|---|---|---|---|
| CNN (ResNet) | 0.758 (0.693-0.822) | 0.941 (0.923-0.960) | 0.0% |
| CNN (VGG) | 0.824 (0.767-0.882) | 0.931 (0.911-0.951) | 0.0% |
| Random Forest | 0.731 (0.637-0.824) | 0.724 (0.622-0.825) | 91.1% |
| Neural Network (MLP) | 0.797 (0.666-0.927) | 0.794 (0.687-0.900) | 97.2% |
Impact of Disease Prevalence on Sensitivity
Meta-regression analysis revealed a statistically significant association between disease prevalence and sensitivity (β = 0.9923, p = 0.050). This suggests that studies with higher disease prevalence tended to report higher sensitivity values, potentially limiting the generalizability of models trained on over-represented outcomes. This highlights the need for balanced datasets in training AI models for broad applicability and robust performance across diverse patient populations.
Enterprise Process Flow: Study Selection
Records identified from databases (n=2468)
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Duplicates removed (n=1076)
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Records screened (n=1392)
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Reports assessed for eligibility (n=75)
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Studies included in review (n=7)
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