Teeth tell tales: a narrative review on dental age estimation methods from traditional to AI-driven approaches

Abstract Background Dental age estimation plays an important role in identifying an unknown person. The evaluation of dental developments is important in occlusion assessment, growth evaluation, and forensic application. In legal investigations, orthodontic planning, and age-sensitive therapeutic procedures, dental age assessment is essential. Traditional techniques are sensitive to subjectivity and variability among observers because they rely on measurements being taken manually and visual inspection. Teeth are highly helpful because they are strong and durable, and because they change significantly from birth to approximately 16–18 years, they can be used to estimate an individual’s age. Main body Radiological techniques are widely employed because they are noninvasive and consistent. By measuring the distance among landmarks such as the pulp, root, and tooth thereafter inserting the results into regression models, dental age can be approximated from accessible radiographs. As an alternative, a table can be used to assess the permanent teeth’s developmental stage and translate it into an estimated age. Research in this area is ongoing, as evidenced by the publication of age group estimating methods that use both human perception and machine learning. Machine learning can autonomously extract age-related information from orthopantomograms and establish a sophisticated, all-encompassing correlation between several features with chronological age. Conclusions Conventional methods depend on observation and manual measurement, which makes them prone to subjectivity and variation among observers. Even while dental development-based classical procedures are credible, they may be enhanced with the use of deep learning, especially neural networks, considering how quickly artificial intelligence technologies are developing. Artificial intelligence’s main component, machine learning, makes dental age prediction more accurate and successful. This narrative review contains a concise summary of the various radiographic approaches that can be used for estimating dental age in living individuals. Artificial intelligence models have demonstrated an impressive level of accuracy in determining dental age across various populations, despite potential biases and disparities in dental development.