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Correlating skeletal and dental developmental stages using radiographic parameters.
Graziela Oro Cericato1, Ademir Franco2, Marcos Alan Vieira Bittencourt3
1Faculty of Dentistry, Meridional Faculty, Brazil.
Journal of Forensic and Legal Medicine
|May 25, 2016
Summary
This study correlates skeletal and dental development in children using radiographs. While moderate correlations exist, especially with the mandibular left second premolar, careful interpretation is needed for accurate age estimation.
Area of Science:
- Forensic Anthropology
- Pediatric Dentistry
- Radiology
Background:
- Increased demand for child age estimation due to globalization and violent crimes.
- Need for reliable methods correlating skeletal and dental development for forensic and clinical applications.
Purpose of the Study:
- To investigate the correlation between skeletal and dental developmental stages using radiographic parameters in children.
- To assess the predictive accuracy of dental development for skeletal maturity.
Main Methods:
- Utilized lateral cephalometric and panoramic radiographs (n=576) from the same individuals.
- Assessed skeletal development using Hassel and Farman (1995) and Baccetti et al. (2002) methods.
- Assessed dental development using Demirjian et al. (1973) method.
- Employed likelihood-ratio tests and Spearman's correlation coefficient to analyze relationships.
Main Results:
- Significant correlations were found between skeletal stage 1 and dental stages E (mandibular canines) and D (mandibular premolars, second molars).
- The mandibular left second premolar showed the highest correlation coefficients (0.652 and 0.646) with skeletal development.
- Correlations, though statistically significant, were generally moderate (<0.652), indicating variability.
Conclusions:
- Skeletal and dental development show moderate correlations, suggesting potential for combined use in age estimation.
- The findings highlight the mandibular left second premolar as a key indicator in correlating dental and skeletal maturity.
- Further research is recommended to explore alternative classifications for stronger predictive correlations in age estimation.
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