Skeletal idiopathic osteosclerosis helps to perform personal identification of unknown decedents: A novel contribution from anatomical variants through CT scan.

2016 
Abstract Personal identification consists of the comparison of ante-mortem information from a missing person with post-mortem data obtained from an unidentified corpse. Such procedure is based on the assessment of individualizing features which may help in providing a conclusive identification between ante-mortem and post-mortem material. Anatomical variants may provide important clues to correctly identify human remains. Areas of idiopathic osteosclerosis (IO), or dense bone islands (DBIs) characterized by radiopaque areas of dense, trabeculated, non-inflamed vital bone represent one of these, potentially individualizing, anatomical features. This study presents a case where the finding of DBI was crucial for a positive identification through CT-scan. A decomposed body was found in an apartment in June 2014 in advanced decomposition and no dental records were available to perform a comparison for positive identification. Genetic tests were not applicable because of the lack of relatives in a direct line. The analysis of the only ante-mortem documentation, a CT-scan to the deceased dating back to August 2009, showed the presence of three DBIs within the trabecular bone of the proximal portion of the right femur. The same bony district was removed from the corpse during the autopsy and analysed by CT-scan, which verified the presence of the same features. Forensic practitioners should therefore be aware of the great importance of anatomical bone variants, such as dense bone islands for identification purposes, and the importance of advanced radiological technique for addressing the individualizing potential of such variants. We propose that anatomical variants of the human skeleton should be considered as being “primary identification characteristics” similar to dental status, fingerprints and DNA.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    25
    References
    10
    Citations
    NaN
    KQI
    []