EC Dental Science

Age estimation is critically important in various fields such as forensic sciences, archaeology, anthropology, and pediatric dentistry for identifying missing children and determining age in forensic cases. Accurate age estimation in children and adolescents is crucial for legal and medical purposes. Dental development and mineralization are reliable chronological age indicators that occur in a specific sequence. Age estimation methods are divided into clinical and radiographic evaluations, with clinical methods assessing tooth eruption and radiographic methods analyzing dental mineralization and development stages. In recent years, techniques like artificial neural networks (ANN) and convolutional neural networks (CNN) have shown great potential in providing objective and accurate age estimates by analyzing large datasets. The integration of artificial intelligence (AI) into dental age estimation promises significant improvements in speed and objectivity, offering a valuable tool for both forensic and clinical dentistry. This study highlights the potential of transforming age estimation processes and lays the foundation for broader applications and further research in AI such as radiomics applications.

 Keywords: Artificial Intelligence; Age Estimation; Radiomics

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