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Background <p>Accurate age estimation is a cornerstone of forensic odontology and pediatric dental care, essential for identifying unknown individuals, resolving legal disputes, and planning treatments. Dental methods are considered superior to skeletal assessment as they are less susceptible to variations caused by ethnicity, nutrition, and endocrine pathologies. This study aimed to develop and validate a novel regression formula to provide a reliable and accurate method for dental age estimation in children.</p> Result <p>A total of 1,111 orthopantomographs of children aged 4–15&#xa0;years were analyzed to create the regression model. The differences between predicted dental age and chronological age were categorized as less than 6&#xa0;months (LT6), between 6&#xa0;months and 1&#xa0;year (MT6M), and more than 1&#xa0;year (MT1yr). Model agreement was assessed with Pearson’s Chi-square and Cohen’s Kappa. The model was subsequently validated on an independent sample of 147 orthopantomographs, with sex-based comparisons performed. From the total dataset, 42.8% of the predicted ages were within 6&#xa0;months of the chronological age (LT6), 24.9% were MT6M, and 32.3% exceeded 1&#xa0;year. Pearson’s Chi-square test demonstrated a highly significant association across the three categories indicating the distribution was not due to chance. However, Cohen’s Kappa value (κ = − 0.063, <i>p =</i> 0.002) reflected poor agreement beyond chance between predicted and actual age categories. In the validation dataset of 147 orthopantomographs, 70.1% of cases showed differences of less than one year between the predicted and actual chronological age, while 29.9% had differences exceeding one year. There was no significant difference between males and females (χ<sup>2</sup> = 0.914, <i>p =</i> 0.339).</p> Conclusion <p>The newly developed regression formula provides a clinically useful tool for dental age estimation, majority of estimates falling within a one-year range. Its demonstrated independence from sex-based variations enhances its practical utility. To solidify its value for forensic and pediatric applications, future research should focus on multi-center validation across diverse geographical and ethnic populations.</p>

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Willems vs. Cameriere methods: a comparative validation and regression formula update for age estimation in Indian children

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Indu Varkey,
  • Jasmin Winnier,
  • Parnaja Valke,
  • Robin Mathew,
  • Kiran Ghule,
  • Kanchanlata Tungare

摘要

Background

Accurate age estimation is a cornerstone of forensic odontology and pediatric dental care, essential for identifying unknown individuals, resolving legal disputes, and planning treatments. Dental methods are considered superior to skeletal assessment as they are less susceptible to variations caused by ethnicity, nutrition, and endocrine pathologies. This study aimed to develop and validate a novel regression formula to provide a reliable and accurate method for dental age estimation in children.

Result

A total of 1,111 orthopantomographs of children aged 4–15 years were analyzed to create the regression model. The differences between predicted dental age and chronological age were categorized as less than 6 months (LT6), between 6 months and 1 year (MT6M), and more than 1 year (MT1yr). Model agreement was assessed with Pearson’s Chi-square and Cohen’s Kappa. The model was subsequently validated on an independent sample of 147 orthopantomographs, with sex-based comparisons performed. From the total dataset, 42.8% of the predicted ages were within 6 months of the chronological age (LT6), 24.9% were MT6M, and 32.3% exceeded 1 year. Pearson’s Chi-square test demonstrated a highly significant association across the three categories indicating the distribution was not due to chance. However, Cohen’s Kappa value (κ = − 0.063, p = 0.002) reflected poor agreement beyond chance between predicted and actual age categories. In the validation dataset of 147 orthopantomographs, 70.1% of cases showed differences of less than one year between the predicted and actual chronological age, while 29.9% had differences exceeding one year. There was no significant difference between males and females (χ2 = 0.914, p = 0.339).

Conclusion

The newly developed regression formula provides a clinically useful tool for dental age estimation, majority of estimates falling within a one-year range. Its demonstrated independence from sex-based variations enhances its practical utility. To solidify its value for forensic and pediatric applications, future research should focus on multi-center validation across diverse geographical and ethnic populations.