Deep learning in sex estimation from a peripheral quantitative computed tomography scan of the fourth lumbar vertebra-a proof-of-concept study.

ABSTRACT: Sex estimation is a key element in the analysis of unknown skeletal remains. The vertebrae display clear sex discrepancy and have proven accurate in conventional morphometric sex estimation. This proof-of-concept study aimed to investigate the possibility to develop a deep learning algorithm for sex estimation even from a single peripheral quantitative computed tomography (pQCT) slice of the fourth lumbar vertebra (L4). The study utilized a total of 117 vertebrae from the Terry Anatomical Collection. There were 58 male and 59 female cadavers, all of the white ethnicity, with the average age at death 49 years and a range of 24 to 77 years. A coronal pQCT scan was taken from the midway of the L4 corpus. Sex estimation was performed in a total of 19 neural network architectures implemented in

SUBMITTER: Oura P 

PROVIDER: S-EPMC10752832 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature