Sex estimation using lateral cephalograms: A Statistical Analysis

Rawad Qaq (Lead / Corresponding author), Scheila Manica, Gavin Revie

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The identification of skeletonized remains requires sex estimation. After the pelvis, skull is considered the best sex predictor in the human skeleton. Lateral cephalograms provide details of the skull’s morphology and previous studies have investigated sex analysing calibrated lateral cephalograms of adults aged 20–55 years. Due to the lack of studies around age 18 as an important legal age, this study aimed to investigate adults aged 18–22 years by exploring linear, angular and areal measurements to investigate the best lateral cephalometric parameters that can be used to create a sex prediction model. A total of 135 uncalibrated lateral cephalograms (68♂, 67♀) of Caucasians (Canadians and Americans) aged 18 – 26.3 years were analysed using ImageJ-V1.51 and SPSS-V22. A number of 22 measurements (linear, angular, areal) were obtained by tracing 9 landmarks (G, V, Op, Ba, N, S, ANS, Po, Or). Only 21 parameters were derived and subjected to statistical analysis. Because most of the samples were aged 18 – 22 years, only 119 subjects (59♂, 60♀) from the total sample have been used in the binary logistic regression analysis to create the model. Sex was estimated with an overall accuracy 82.4%. Three angular measurements (ΔN-S-V, ΔNS-G, ΔG-N-S) and one ratio for two areal measurements (G-V-Op-Ba-G/N-S-Ba-ANSN) were the best parameters. Cranio-facial parameters contribute in sex estimation, mainly angular measurements when subjected to logistic regression analysis. Uncalibrated lateral cephalograms can be used to estimate the sex of subjects aged 18–26 years, considering limitations such as bias, ethnicity, and radiological techniques.
Original languageEnglish
Pages (from-to)1-8
Number of pages8
JournalForensic Science International: Reports
Early online date19 Sept 2019
Publication statusPublished - Nov 2019


  • Forensic science
  • Identification
  • Skull
  • Sex estimation
  • Lateral cephalogram
  • Binary logistic regression analysis


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