International Journal of Medical Toxicology and Forensic Medicine (Jan 2023)

Estimation of Gender and Age Based on Three-dimensional Computed Tomography Scan Indices of the Twelfth Thoracic Vertebrae and the First and Fifth Lumbar Vertebrae in Iranian Adults

  • Seyed Reza Saadat Mostafavi,
  • Ramin Emami,
  • Azadeh Memarian,
  • Siamak Soltani,
  • Omid Motamedi,
  • Mohammadreza Khaleghi,
  • Shirin Habibi

Journal volume & issue
Vol. 13, no. 1

Abstract

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Background: Gender identification is a crucial starting point in creating a biological profile for human skeletal remains because it reduces the number of possible matches by 50%. The vertebrae (especially the chest and back) can also be some of the best-preserved skeletal elements in some areas of forensics and archeology. In the present study, gender and age were assessed based on the measurement of three-dimensional computed tomography (CT) scan indices of the Twelfth thoracic (T12) vertebrae and the first and fifth lumbar (L1 and L5) vertebrae in Iranian adults.Methods: The present study was a descriptive study carried out on 200 participants over 18 years of age in 2020. Individuals measuring thoracic and lumbar vertebrae diameters (T12 and first and fifth lumbar vertebrae) by three-dimensional computed tomography (CT) scan (Toshiba, Japan, 16-Slice) with multiplanar reconstruction (MPR) and volume rendering were placed in two sagittal and horizontal sections.Results: The mean age of male and female participants was 34.62±9.63 years and 34.10±9.70 years, respectively, which were not significantly different (P=0.789). The present study showed that the mean indices for T12, L1 and L5 vertebrae were significantly higher in males (P>0.05). The results also showed that T12, L1, and L5 indices of nuts are not good predictors for age estimation.Conclusion: Based on the results, the indices of the T12 vertebrae and the L1, and L5 vertebrae can be used to determine gender, but these indices are not a good criterion to estimate age and do not have the necessary accuracy to predict the age variable

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