ArticleInternational journal of legal medicine2023
Sex estimation from long bones: a machine learning approach.
Article in International journal of legal medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in forensic science: a systematic review. Part I: personal identification.International journal of legal medicine · 2026Pooled it
- Sex estimation from morphometry of lumbar vertebrae (L4-L5): A machine learning approach.International journal of legal medicine · 2026Article
- Constancy of Sexual Dimorphism in the Infracranial Skeleton.American journal of biological anthropology · 2026Article
- Article
- Population-specific validation of long bone linear discriminant analysis against morphoscopic pelvic methods for sex estimation in contemporary Italian skeletal remains.Journal of forensic sciences · 2026Article
- Diagnostic Accuracy of Sternum Measurements for Sex Estimation: A Systematic Review with Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment.Journal of imaging informatics in medicine · 2026Article
- Radiographic femoral measurements for sex and species classification in dogs and cats using machine learning.Journal of veterinary science · 2026Article
- Interpretable machine learning for individualized sex estimation from long bones.International journal of legal medicine · 2026Article
- A geometric morphometrics approach to sex estimation of infants from 0 to 6 years using the auricular surface.Scientific reports · 2026Article
- Metric analysis of the postcranial skeleton: a comprehensive approach for biological sex estimation in an Italian population.International journal of legal medicine · 2026Article
- Sex estimation from patellar measurements in a contemporary Italian population: a machine learning approach.International journal of legal medicine · 2025Article
- Sexual dimorphism of the humerus bones in a French sample: comparison of several statistical models including machine learning models.International journal of legal medicine · 2025Article
- A statistical evaluation of the sexual dimorphism of the acetabulum in an Iberian population.International journal of legal medicine · 2025Article
- Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science.Diagnostics (Basel, Switzerland) · 2024Article
- Sex determination through maxillary dental arch and skeletal base measurements using machine learning.Head & face medicine · 2024Article
- No Bones About It: Sex Is Binary.Archives of sexual behavior · 2024Article
- Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features.PloS one · 2024Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Sex estimation from skeletal remains is one of the crucial issues in forensic anthropology. Long bones can be a valid alternative to skeletal remains for sex estimation when more dimorphic bones are absent or degraded, preventing any estimation from the first intention methods. The purpose of this study was to generate and compare classification models for sex estimation based on combined measurement of long bones using machine learning classifiers. Eighteen measurements from four long bones (radius, humerus, femur, and tibia) were taken from a total of 2141 individuals. Five machine learning methods were employed to predict the sex: a linear discriminant analysis (LDA), penalized logistic regression (PLR), random forest (RF), support vector machine (SVM), and artificial neural network (ANN). The different classification algorithms using all bones generated highly accuracy models with cross-validation, ranging from 90 to 92% on the validation sample. The classification with isolated bones ranked between 83.3 and 90.3% on the validation sample. In both cases, random forest stands out with the highest accuracy and seems to be the best model for our investigation. This study upholds the value of combined long bones for sex estimation and provides models that can be applied with high accuracy to different populations.
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