Dental Attrition-based Age Estimation: A Forensic Analysis using the ASA Method
DOI:
https://doi.org/10.48165/jfmt.2026.43.03.12Keywords:
Forensic Anthropology, Dental Age Estimation, Molar Wear Patterns, ASA, Forensic OdontologyAbstract
Background: Forensic identification relies heavily on age assessment, especially when dealing with unidentified individuals and medico-legal investigations. Dental tissues are trustworthy markers of chronological age because they are extremely resilient to environmental and post-mortem alterations. The Average Surface Attrition (ASA) approach has demonstrated promise for forensic applications and uses molar wear patterns to estimate age.
To assess the ASA method's accuracy in estimating dental age and ascertain how certain biological and environmental factors affect tooth attrition.
Materials and Methods: Sixty participants between the ages of 18 and 45 were split equally into three age groups (18–25, 25–35, and 35–45) for a cross-sectional study. The ASA grading system was used to evaluate the dental attrition of mandibular molars, and published regression models were used to estimate age. Body mass index (BMI), origin, food habits, number of siblings, and age difference between siblings were all recorded. Descriptive statistics, independent t-tests, Pearson's correlation, and Spearman's rank correlation were among the statistical studies performed.
Results: There was a significant positive association (r = 0.9418) between estimated dental age and chronological age. The estimated and actual ages did not differ significantly (p > 0.05), suggesting high predictive accuracy. While dietary type did not significantly affect attrition patterns, place of origin showed a moderate association with dental wear. BMI and sibling age gap demonstrated negligible correlations with attrition.
Conclusion: ASA method for forensic age determination is a dependable, non-invasive, and economical approach. Nevertheless, biological and environmental factors may influence attrition patterns, highlighting the necessity of population-specific validation to increase precision.
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