Efficacy of artificial intelligence in radiographic dental age estimation of patients undergoing dental maturation: A systematic review and meta-analysis

Abstract

Background

Dental age (DA) estimation, crucial for appropriate orthodontic and paediatric treatment planning, traditionally relies on the analysis of developmental stages of teeth. Artificial intelligence (AI) has been increasingly employed for DA estimation through dental radiographs. The current study aimed to systematically review the literature on the application of AI models for radiographic DA estimation among subjects undergoing dental maturation.

Material and methods

The electronic search was conducted through five databases, namely PubMed, Embase, Scopus, Web of Science, and Google Scholar, in July 2024. The search sought studies relying on AI models for DA estimation based on dental radiographs. Data were analysed using STATA software V.14 and heterogeneity was evaluated using I-squared statistics. A random-effects model was employed for meta-analysis. Publication bias was assessed using a funnel plot, Egger's test, Begg's test, and the trim-and-fill method. Heterogeneity was evaluated with a Galbraith plot, and sensitivity analysis tested robustness.

Results

Thirteen studies were deemed eligible for qualitative synthesis, seven of which were included in the meta-analysis. The mean absolute error varied from 0.6915 to 12.04, with accuracy between 0.404 and 0.959. Sensitivity ranged from 0.42 to 1.00, specificity ranged from 0.8014 to 0.982, and positive predictive value ranged from 0.43 to 0.90. The pooled accuracy of seven studies equalled 0.85 (95% CI: 0.79–0.91).

Conclusion

The present findings support the effectiveness of AI models in DA estimation of individuals under 25 years old based on their dental radiographs. However, further studies with larger sample sizes for both test and training datasets are suggested to validate the reliability and clinical applicability of AI in DA estimation.