CT‐based sex estimation on human femora using statistical shape modeling

Bibliographic Data

ID8312890
AuthorsBarbara Fliss (0000-0002-9501-993X, Institute of Forensic Medicine University of Zurich Zurich Switzerland, corresponding author), Marcel Luethi (Department of Mathematics and Computer Science University of Basel Basel Switzerland), Philipp Fuernstahl (Universitätsklinik Balgrist, Balgrist University Hospital Zurich Switzerland), Angi M Christensen (Federal Bureau of Investigation Laboratory Quantico Virginia), Ken Sibold (Institute of Forensic Medicine University of Zurich Zurich Switzerland), Michael J Thali (0000-0002-2613-6956, University of Zurich), Michael Thali (Institute of Forensic Medicine University of Zurich Zurich Switzerland), Lars C Ebert (Institute of Forensic Medicine University of Zurich Zurich Switzerland), Lars Ebert (0000-0002-8215-7006, University of Zurich)
Year2019
Volume169
Issue2
Pages279-286
Publication date2019-06-01
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Physical Anthropology (JOURNAL)
Journal identifiersISSN: 0002-9483 • E-ISSN: 1096-8644
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ajpa.23828
PMID30927271
OpenAlexW2928214695
LanguageEN
Citations received1
References cited47

OBJECTIVES: Estimating the sex of decomposed corpses and skeletal remains of unknown individuals is one of the first steps in the identification process in forensic contexts. Although various studies have considered the femur for sex estimation, the focus has primarily been on a specific single or a handful of measurements rather than the entire shape of the bone. In this article, we use statistical shape modeling (SSM) for sex estimation. We hypothesize that the accuracy of sex estimation will be improved by using the entire shape. MATERIALS AND METHODS: For this study, we acquired a total of 61 femora from routine postmortem CT scans at the Institute for Forensic Medicine of the University of Zurich. The femora were extracted using segmentation technique. After building a SSM, we used the linear regression and nonlinear support vector machine technique for classification. RESULTS: Using linear logistic regression and only the first principal component of the SSM, 76% of the femora were correctly classified by sex. Using the first five principal components, this value could be increased to 80%. Using nonlinear support vector machines and the first 20 principal components increased the rate of correctly classified femora to 87%. DISCUSSION: Despite some limitations, the results obtained by using SSM for sex estimation in femur were promising and confirm the findings of other studies. Sex estimation accuracy, however, is not significantly improved over single or multiple linear measurements. Further research might improve the sex determination process in forensic anthropology by using SSM

Estimation · Femur · Forensic anthropology · Geography · Linear regression · Logistic regression · Nonlinear system · Pattern recognition (psychology) · Principal component analysis · Statistics · Support vector machine · Artificial Intelligence · Autopsy Techniques and Outcomes · Computer Science · Engineering · Forensic Anthropology and Bioarchaeology Studies · Mathematics · Medicine · Paleopathology and ancient diseases · Surgery

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