| ORIGINAL ARTICLE |
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| Year : 2019 | Volume
: 68
| Issue : 2 | Page : 153-158 |
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Teeth as an anatomical modality for age estimation using radiographic approach
Kusum Singal1, Neelkamal Sharma1, Permila Singh2, Vikas Kumar1
1 Department of Genetics, MDU, Rohtak, Haryana, India
2 Department of Statistics, GCW, Rohtak, Haryana, India
Correspondence Address:
Dr. Neelkamal Sharma
Department of Genetics, MDU, Rohtak, Haryana
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/JASI.JASI_89_19
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Introduction: Age estimation of adult individuals represents an important part of forensic anthropology, forensic medicine, forensic osteology, and forensic dentistry. Teeth proved to be a perfect anatomical tool for age estimation. The study was performed with aim to evaluate the coronal pulp cavity index (CPCI) using radiographic approach and to correlate the CPCI with the real age, i.e. chronological age of the individuals. Material and Methods: CPCI was radiographically evaluated using radiovisiographs (RVGs) of 320 individuals (160 males and 160 females) having age range of 15–54 years. The sample comprised of 1280 RVGs of 4 teeth per individual (maxillary canine, maxillary second premolar, mandibular canine, and mandibular first premolar). Two radiographic measurements were performed on all radiographs. One is coronal pulp height and another one is height of crown (coronal height). CPCI for each radiograph was calculated and correlated with the chronological age of the study individuals using statistical software SPSS (Version 21). Results: Intra-observer agreement of CPCI measurements was almost perfect. The accuracy of regression models, when applied to different set of radiograph samples, was within acceptable range of differences in the forensic anthropology. Discussion and Conclusions: All the selected teeth showed a strong negative correlation with the chronological age. However, all selected teeth do not have equal applicability for age estimation suggesting that further evaluation on different samples for teeth specific regression models for age estimation.
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