ArticleScientific reports2024
Deep learning versus human assessors: forensic sex estimation from three-dimensional computed tomography scans.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Deep learning-based sex estimation from multi-planar cranial CT images in a Thai population.Anatomical science international · 2026Article
- AI-based organ weight estimation from postmortem computed tomography.Scientific reports · 2026Article
- Paranasal Sinus Morphometry for Forensic Sex Estimation: A Computed Tomography Study of 499 Individuals with a Cross-Validated, Transparently Reported Machine Learning Model.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluating the accuracy of sex estimation from human tooth volume: leveraging automated AI segmentation and comparative analysis of machine learning algorithms.Head & face medicine · 2026Article
- 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
- Automatic Personal Identification Using a Single MRI Slice.Bioengineering (Basel, Switzerland) · 2026Article
- YOLO11m-cls applied to sex and age classification based on the radiographic analysis of the nasal aperture.Scientific reports · 2025Article
- Commentary on Special Issue: "Towards a Biocultural Synthesis of the Peopling of the Americas".American journal of biological anthropology · 2025Article
- Classifying Sex from MSCT-Derived 3D Mandibular Models Using an Adapted PointNet++ Deep Learning Approach in a Croatian Population.Journal of imaging · 2025Article
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7 authors.
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Abstract
Cranial sex estimation often relies on visual assessments made by a forensic anthropologist following published standards. However, these methods are prone to human bias and may be less accurate when applied to populations other than those for which they were originally developed with. This study explores an automatic deep learning (DL) framework to enhance sex estimation accuracy and reduce bias. Utilising 200 cranial CT scans of Indonesian individuals, various DL network configurations were evaluated against a human observer. The most accurate DL network, which learned to estimate sex and cranial traits as an auxiliary task, achieved a classification accuracy of 97%, outperforming the human observer at 82%. Grad-CAM visualisations indicated that the DL model appears to focus on certain cranial traits, while also considering overall size and shape. This study demonstrates the potential of using DL to assist forensic anthropologists in providing more accurate and less biased estimations of skeletal sex.
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