Evidence-Based Performance of Artificial Intelligence in Dental Age Assessment: A Systematic Review and Meta-Analysis Using Orthopantomograms

Background: Chronological age estimation based on biological markers plays a crucial role in forensic, legal, and clinical contexts. Among available methods, dental age assessment (DAA) using orthopantomograms (OPGs) is considered one of the most accurate approaches. However, conventional DAA methods rely on expert interpretation, introducing subjectivity and limiting reproducibility. In recent years, artificial intelligence (AI) has emerged as a promising solution to overcome these limitations. This systematic review with meta-analysis aimed to evaluate the performance of AI-based models for dental age assessment using OPGs. Methods: The review protocol followed PRISMA guidelines and was registered in PROSPERO. A total of 27 studies, published between 2020 and 2025, were included, encompassing a wide range of machine learning and deep learning approaches, predominantly convolutional neural networks (CNNs). Results: For chronological age estimation, the pooled root mean squared error (RMSE) was 1.76 years, indicating that AI models can predict age with an average deviation of less than two years from true chronological age. The pooled coefficient of determination (R²) was 0.92, reflecting strong agreement and high explanatory power between predicted and actual ages. In classification tasks related to legal age thresholds, AI models demonstrated high discriminatory performance, with an overall area under the curve (AUC) exceeding 0.90 and a high diagnostic odds ratio (DOR). Hierarchical summary receiver operator characteristic analyses (HSROC) revealed high pooled sensitivity and specificity, with a slightly stronger ability to correctly classify individuals below the legal threshold. Network meta-analyses consistently ranked human expert assessments lower than AI-based models, especially CNN architectures such as EfficientNet, ResNet, and VGG16, although differences were not statistically significant. Conclusions: Despite substantial methodological heterogeneity across studies, the findings suggest that AI-based models for DAA using OPGs have reached a level of accuracy and robustness suitable for forensic and legal applications. AI systems should be regarded as complementary tools, enhancing objectivity, reproducibility, and consistency in age estimation, being good decision-support tools in legal and forensic settings.