Anthropometric characteristics of scapula for sex determination using CT scans images in Iranian population. (December 2020)
- Record Type:
- Journal Article
- Title:
- Anthropometric characteristics of scapula for sex determination using CT scans images in Iranian population. (December 2020)
- Main Title:
- Anthropometric characteristics of scapula for sex determination using CT scans images in Iranian population
- Authors:
- Ghasemi, Behzad
Ramezani, Rojin
Katourani, Neda
Babahajian, Asrin
Yousefinejad, Vahid - Abstract:
- Highlights: A significant difference was observed in all measured anthropometric indices of scapula between the sexes. RLML with 90.5% and LMSB, LMLS, and LLML with 89%, each had the highest accuracy in determining sex. the combination of GCH and LML indices in the right scapula and the combination of MSH, MLS, GCB, and LSL indices in the left scapula with 92.5% and 91% diagnostic accuracy, respectively, were chosen for sex determination. Abstract: Background: Identification in forensic medical science is challenging. Therefore, the aim of this study is to determine the role of anthropometric variables of scapula using computed tomography (CT) images for identification. Materials and Methods: In this study, the anthropometric parameters of the right and left shoulder bones were measured on CT images of 200 Iranian men and women aged 20 to 60 years. Then statistical analysis was performed to assess whether these parameters can be used for sex determination. Discriminating function analysis was used to predict the model and its formula. Results: A significant difference was observed in all measured anthropometric indices between the sexes (p < 0.001). Among the measured indices, RLML with 90.5% and LMSB, LMLS, and LLML with 89%, each had the highest accuracy in determining sex. In the stepwise diagnostic analysis model, the combination of GCH and LML indices in the right scapula and the combination of MSH, MLS, GCB, and LSL indices in the left scapula with 92.5% and 91%Highlights: A significant difference was observed in all measured anthropometric indices of scapula between the sexes. RLML with 90.5% and LMSB, LMLS, and LLML with 89%, each had the highest accuracy in determining sex. the combination of GCH and LML indices in the right scapula and the combination of MSH, MLS, GCB, and LSL indices in the left scapula with 92.5% and 91% diagnostic accuracy, respectively, were chosen for sex determination. Abstract: Background: Identification in forensic medical science is challenging. Therefore, the aim of this study is to determine the role of anthropometric variables of scapula using computed tomography (CT) images for identification. Materials and Methods: In this study, the anthropometric parameters of the right and left shoulder bones were measured on CT images of 200 Iranian men and women aged 20 to 60 years. Then statistical analysis was performed to assess whether these parameters can be used for sex determination. Discriminating function analysis was used to predict the model and its formula. Results: A significant difference was observed in all measured anthropometric indices between the sexes (p < 0.001). Among the measured indices, RLML with 90.5% and LMSB, LMLS, and LLML with 89%, each had the highest accuracy in determining sex. In the stepwise diagnostic analysis model, the combination of GCH and LML indices in the right scapula and the combination of MSH, MLS, GCB, and LSL indices in the left scapula with 92.5% and 91% diagnostic accuracy, respectively, were chosen for sex determination. Conclusion: Measuring the dimensions of the scapula using a CT scan can be useful in sexual dimorphism with high-precision. … (more)
- Is Part Of:
- Forensic Imaging. Volume 23(2020)
- Journal:
- Forensic Imaging
- Issue:
- Volume 23(2020)
- Issue Display:
- Volume 23, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 23
- Issue:
- 2020
- Issue Sort Value:
- 2020-0023-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-12
- Subjects:
- Anthropometric indices -- Scapula -- CT scan -- Sexual Dimorphism -- Iranian Population
- Journal URLs:
- http://www.sciencedirect.com/ ↗
- DOI:
- 10.1016/j.fri.2020.200408 ↗
- Languages:
- English
- ISSNs:
- 2666-2256
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23742.xml