YOLO11m-cls applied to sex and age classification based on the radiographic analysis of the nasal aperture
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Summary
This summary is machine-generated.Deep learning using Convolutional Neural Networks (CNNs) showed 74% accuracy in estimating sex and age from nasal aperture radiographs. Accuracy was lower in younger individuals, limiting its use for subadult sex estimation.
Area Of Science
- Forensic anthropology
- Medical imaging analysis
- Artificial intelligence in forensics
Background
- Computer vision and deep learning offer novel approaches to radiographic analysis.
- These methods are being explored for forensic applications like sex and age estimation.
- The nasal aperture is a potential morphological feature for such estimations.
Purpose Of The Study
- To evaluate the diagnostic accuracy of a Convolutional Neural Network (CNN) for sex and age classification using radiographic images.
- To assess the utility of the nasal aperture as a feature for sex estimation via deep learning.
Main Methods
- A Convolutional Neural Network (CNN) architecture (YOLO11m-cls) was implemented for image classification.
- The study utilized 9,349 radiographs of the nasal aperture region.
- Training involved 5-fold cross-validation to ensure robust performance evaluation.
Main Results
- The CNN achieved an overall accuracy of 74% (ROC AUC of 0.74).
- Classification accuracy was 73% for males and 75.17% for females.
- Accuracy decreased by 10% in younger individuals, with limited applicability for subadult sex estimation.
Conclusions
- The nasal aperture shows limited applicability for sex estimation in the studied population using this deep learning model.
- Reduced expression of sexually dimorphic traits in younger individuals impacts accuracy.
- Findings support current recommendations against sex estimation in subadults based on this method.
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