Enterprise AI Analysis
Maxillary sinus classification for sex and age using 23 artificial intelligence architectures
This study evaluated 23 deep learning models (21 CNNs, 2 ViTs) for sex and age classification based on maxillary sinus panoramic radiographs. Transformers (DeiT, ViT) consistently outperformed CNNs for binary sex and age classification, achieving accuracies up to 80.7% for sex and 95.3% for age. Multiclass classification was less accurate (up to 75.4%), suggesting current limitations for complex tasks in forensic practice, but binary classification shows promise as an adjunct tool.
Unlocking Forensic Potential with Advanced AI
This research provides critical insights into the application of deep learning for human identification, offering substantial advantages in speed and accuracy for forensic dentistry and anthropology.
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Sex Classification Accuracy
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Age Classification Accuracy
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Models Evaluated
Deep Analysis & Enterprise Applications
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The study employed a comparative deep learning approach using 23 different architectures (21 CNNs and 2 Vision Transformers) to classify individuals by sex and age based on panoramic radiographs of the maxillary sinuses. A 5-fold cross-validation strategy ensured robust model evaluation. Image preprocessing included anonymization, cropping, resizing to 224x224 pixels, and various augmentation techniques.
For sex classification, Transformer-based architectures, DeiT (80.7% accuracy) and ViT (80.6% accuracy), showed superior performance, followed by EfficientNetV2M (78.1%). These models consistently outperformed conventional CNNs. The multiclass approach for sex and age combined yielded lower accuracies, highlighting the complexity of simultaneous classification.
80.7%
Max Sex Classification Accuracy (DeiT)
| Model | Accuracy | F1-Score | Female Accuracy | Male Accuracy |
|---|---|---|---|---|
| DeiT | 80.7% | 79.1% | 85% | 75% |
| ViT | 80.6% | 78.5% | 82% | 79% |
| EfficientNetV2M | 78.1% | 75.1% | 85% | 71% |
| YOLOv11 | 75.4% | 75.6% | N/A | N/A |
Age classification (≤15 vs. >15 years) achieved higher accuracies, with YOLOv11 leading (95.3%), followed by ViT (94.9%) and DeiT (94.6%). Transformers again showed competitive performance, but YOLOv11 demonstrated a slight edge. This suggests that age estimation is a more distinguishable task for AI models in this context.
95.3%
Max Age Classification Accuracy (YOLOv11)
Enterprise Process Flow
Image Acquisition
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ROI Annotation
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Preprocessing & Augmentation
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Model Training (23 Architectures)
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5-Fold Cross-Validation
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Performance Evaluation
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Age Classification Result
Binary sex and age classification models show promise as adjunct tools in forensic contexts, particularly due to the consistent performance of Transformer-based architectures. However, multiclass applications require further research before widespread recommendation, as current accuracy levels are insufficient for definitive forensic identification. The study highlights the need for larger, more diverse datasets and refined age categorization for future advancements.
Future Directions in Forensic AI
While promising, the direct application of AI models for definitive forensic identification is still under development. This study sets a baseline for using advanced deep learning techniques, emphasizing areas for future research.
- Need for broader age categories (e.g., 1-year intervals) for more precise age estimation.
- Incorporation of 3D imaging (CT) to overcome limitations of 2D radiographs.
- Development of multi-centric validation studies to ensure generalizability across different populations and equipment.
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Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A clear, phased approach to integrating advanced AI into your forensic operations, ensuring seamless adoption and measurable results.
Phase 1: Initial Consultation & Strategy
Understand your current forensic workflow, data landscape, and specific classification needs (sex, age, individual identification). Define clear objectives and success metrics.
Phase 2: Data Preparation & Model Customization
Assist in anonymizing, preprocessing, and augmenting your specific radiographic datasets. Customize and fine-tune top-performing AI architectures (DeiT, ViT, YOLOv11) for optimal accuracy on your data.
Phase 3: Integration & Validation
Integrate the customized AI models into your existing systems. Conduct rigorous validation and testing against a diverse range of unseen data to ensure robustness and compliance with forensic standards.
Phase 4: Training & Deployment
Provide comprehensive training for your team on model usage and interpretation. Deploy the AI solution for real-world application, offering ongoing support and performance monitoring.
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