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A new method for sex estimation based on femoral cross-sectional geometry measurements and its validation using recent and ancient populations.

Samuel FrancisYulia MakoviychuckLiron ChavoinikSarah BorgelAriel PokhojaevVictoria RoulNathan PeledHila May
Published in: International journal of legal medicine (2023)
Estimating sex is a fundamental task in biological and forensic anthropology. This study aimed to develop new methods for sex estimation based on femoral cross-sectional geometry (CSG) variables and to test their applicability in recent and ancient assemblages. The sample was divided into a study group (living individuals, N = 124) for creating sex prediction equations and two test groups: living individuals (N = 31) and prehistoric individuals (N = 34). The prehistoric sample was divided into three subgroups according to subsistence strategy (hunter-gatherers, early farmers that also hunted, and farmers and herders). Femoral CSG variables (size, strength, and shape) were measured from CT images using dedicated software. Discriminant functions for sex estimation were calculated for various bone completeness scenarios and validated using the test groups. Size and strength parameters were sexually dimorphic, while shape was not. Discriminant functions for sex estimation produced success rates in the living sample between 83.9 and 93.5%; the distal shaft yielded the highest results. Success rates were lower among the prehistoric test sample, with better results (83.3%) for the mid-Holocene population (farmers and herders) than for earlier groups (e.g., hunter-gatherers; < 60%). These results were compared with those obtained using other methods for sex estimation based on various skeletal elements. This study provides new, reliable, and simple methods with high success rates for sex estimation based on femoral CSG variables obtained automatically from CT images. Discriminant functions were created for various conditions of femoral completeness. However, these functions should be used carefully in past populations from different settings.
Keyphrases
  • cross sectional
  • computed tomography
  • deep learning
  • climate change
  • magnetic resonance imaging
  • machine learning
  • magnetic resonance
  • body composition
  • positron emission tomography
  • image quality
  • dual energy