Forensic Anthropology
YOLO11m-cls applied to sex and age classification based on the radiographic analysis of the nasal aperture
This study evaluates the diagnostic accuracy of a Convolutional Neural Network (CNN) (YOLO11m-cls) for classifying sex and age from radiographic images of the nasal aperture. Analyzing 9,349 radiographs (ages 6-22.9 years), the model achieved an overall accuracy of 74% for sex classification (73% for males, 75.17% for females) and an AUC of 0.74. Accuracy for age classification (under/over 15 years) was 83% for older individuals and 89.5% for younger. The study highlights limited applicability of the nasal aperture for sex estimation, particularly in subadults, due to reduced sexual dimorphism in younger individuals and an accuracy rate corresponding to one misclassification out of every four predictions.
Key Executive Impact
Our analysis reveals critical metrics for implementing AI in forensic anthropology based on this research.
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Overall Sex Classification Accuracy
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AUC for Sex Classification
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Age Classification Accuracy (>15 years)
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Age Classification Accuracy (≤15 years)
Deep Analysis & Enterprise Applications
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74%
Overall Sex Classification Accuracy
1 misclassification
out of every 4 predictions
Enterprise Process Flow
Image Anonymization & Preprocessing
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Nasal Aperture ROI Annotation
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YOLO11m-cls Training (5-fold CV)
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Performance Evaluation (Accuracy, AUC, Grad-CAM)
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Sex & Age Classification
| Feature | Accuracy | Comments |
|---|---|---|
| Nasal Aperture (this study) | 74% | Limited dimorphism, especially in subadults. |
| Adult Mandibles (Machine Learning) | Up to 95% | Higher accuracy, particularly in mature specimens. |
| Cranial Measurements (Statistical Models) | High | Combines multiple features, often adult-specific. |
10%
decrease in accuracy for younger individuals (under 12 years)
Implications for Forensic Practice
While deep learning offers a promising avenue for forensic odontology, the study emphasizes that expert confirmation remains indispensable. The nasal aperture, in isolation, is not a robust tool for definitive sex or age assessment, especially in subadults. Future research should explore external validation with diverse populations and imaging protocols to address potential biases and enhance generalizability.
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Annual Savings
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