Forskning ved Københavns Universitet - Københavns Universitet


Stature Estimation from Postmortem CT Femoral Maximum Length in Contemporary Danish Population

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Stature estimation methods for Danish adult population have generally relied on Trotter and Gleser's and Boldsen's regression equations that are based on the skeletal remains of recent war dead American Whites, Terry Skeletal Collection, and Danish archaeological medieval skeletal materials, respectively. These equations are probably not suitable for stature estimation in contemporary Danish forensic cases. Furthermore, because postmortem computed tomography (PMCT) is now routinely performed at Danish forensic departments, equations based on PMCT, rather than measurements of defleshed bones, are needed. The aim of this study was to develop new equations for adult stature estimation based on PMCT femoral measurement. Maximum femoral length was measured on the PMCT images of 78 individuals (41 males and 37 females) aged 23-45 years. The measurement accuracy was tested on dry bones, and all the measurements were included in the inter- and intra-observer analyses. Both analyses results demonstrated the reliability of the method and data. Comparison between the living stature of the individuals and the estimates based on the equations by Trotter and Gleser and Boldsen demonstrated the unreliability of the previous equations to some extent. New regression equations were then developed and validated on a different sample of 18 Danish forensic cases. Comparisons of all the equations indicated that both the sets of previous equations underestimated the stature in the new validation dataset. The new equations developed in this study provide a reliable alternative for stature estimation in modern Danish forensic cases.

TidsskriftJournal of Forensic Sciences
Udgave nummer3
Sider (fra-til)930-938
Antal sider9
StatusUdgivet - 2020

Bibliografisk note

© 2019 American Academy of Forensic Sciences.

ID: 232063715