Abstract
There is renewed interest in Asia for the development of forensic anthropological standards, partly due to the need for population-specific models to maintain high classification accuracies. At present, there are no known studies utilising morphoscopic standards specific to the Indonesian population. Craniometric analyses can often be time-consuming; morphoscopic assessments are quicker and are also known to be reliable and accurate. One of the most utilised morphoscopic standards for the estimation of skeletal sex is that of Walker (2008). Its application across population groups demonstrated reduced accuracies outside of the United States; population-specific predictive models would thus serve to improve the identification process of unknown skeletal remains. Digital imaging also allows for the validation of standards on a contemporary population and is an appropriate proxy to physical skeletal material.
The present study quantifies the applicability of the Walker standard to a contemporary Indonesian population. A total of 200 cranial MSCT scans from a hospital in Makassar were analysed using OsiriX®. Scoring was performed in accordance with the Walker standard. Five univariate and nine multivariate predictive models were derived using single trait and multi-trait combinations. The best performing univariate model included the glabella, with a total classification accuracy of 82.0% and a sex bias of 14.6%. Classification accuracy with all traits considered was at 95.2% for females and 82.8% for males with a sex bias of 12.5%. These results provide practitioners in Indonesia with an appropriate morphoscopic sex estimation standard, strengthening their capabilities in the field and improving judicial outcomes.
The present study quantifies the applicability of the Walker standard to a contemporary Indonesian population. A total of 200 cranial MSCT scans from a hospital in Makassar were analysed using OsiriX®. Scoring was performed in accordance with the Walker standard. Five univariate and nine multivariate predictive models were derived using single trait and multi-trait combinations. The best performing univariate model included the glabella, with a total classification accuracy of 82.0% and a sex bias of 14.6%. Classification accuracy with all traits considered was at 95.2% for females and 82.8% for males with a sex bias of 12.5%. These results provide practitioners in Indonesia with an appropriate morphoscopic sex estimation standard, strengthening their capabilities in the field and improving judicial outcomes.
Original language | English |
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Pages (from-to) | 1559-1571 |
Number of pages | 13 |
Journal | International Journal of Legal Medicine |
Volume | 138 |
Issue number | 4 |
Early online date | 1 Feb 2024 |
DOIs | |
Publication status | Published - Jul 2024 |