| Abstract: |
Background: Artificial intelligence (AI) methods, including machine learning and deep learning, are increasingly applied in orthodontics for tasks like assessing skeletal maturity. Accurate timing of treatment is crucial, but traditional methods such as cervical vertebral maturation (CVM) staging have limitations due to observer variability and complexity. AI has the potential to automate CVM assessment, enhancing reliability and user-friendliness. This systematic review and meta-analysis aimed to evaluate the overall performance of artificial intelligence (AI) models in assessing cervical vertebrae maturation (CVM) in radiographs, when compared to clinicians. Methods: Electronic databases of Medline (via PubMed), Google Scholar, Scopus, Embase, IEEE ArXiv and MedRxiv were searched for publications after 2010, without any limitation on language. In the present review, we included studies that reported AI models’ performance on CVM assessment. Quality assessment was done using Quality assessment and diagnostic accuracy Tool-2 (QUADAS-2). Quantitative analysis was conducted using hierarchical logistic regression for meta-analysis on diagnostic accuracy. Subgroup analysis was conducted on different AI subsets (Deep learning, and Machine learning). Results: A total of 1606 studies were screened of which 25 studies were included. The performance of the models was acceptable. However, it varied based on the methods employed. Eight studies had a low risk of bias in all domains. Twelve studies were included in the meta-analysis and their pooled values for sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for each cervical stage (CS). The most accurate CVM evaluation was observed for CS1, boasting a sensitivity of 0.87, a specificity of 0.97, and a DOR of 213. Conversely, CS3 exhibited the lowest performance with a sensitivity of 0.64, and a specificity of 0.96, yet maintaining a DOR of 32. Conclusion: AI has demonstrated encouraging outcomes in CVM assessment, achieving notable accuracy. © The Author(s) 2025. |
