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Comparison among artificial intelligence-based age estimation from morphological analysis of the pubic symphysis versus experienced and novice practitioners using a new atlas for component labeling
This technical note evaluates a new atlas for labeling pubic symphysis components in forensic age estimation. It compares AI-based age estimation with macroscopic observations by experienced and novice practitioners. The study finds that while the new atlas is effective for component labeling, especially for experienced practitioners (Kappa > 0.6), novice practitioners show higher intra- and inter-observer errors (Kappa < 0.6). AI achieves comparable accuracy to human practitioners, suggesting its potential to reduce subjectivity and dependency on observer experience. The results are preliminary, calling for more extensive evaluation.
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Accuracy Improvement (AI vs. Novice)
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Observer Error Reduction (Experienced vs. Novice)
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Methodology
The study's methodology focuses on enhancing the reliability and objectivity of age estimation through a novel atlas and AI integration.
Automated Age Estimation Process
New Atlas for Pubic Symphysis Component Labeling
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Human Labeling by Novice & Experienced Practitioners
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Todd's Method Phase Assignment (Manual)
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C4.5 Algorithm Training (from Human Labels)
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Semi-Automatic AI Age-at-Death Estimation
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Comparison of AI vs. Human Macroscopic Estimates
Findings
Key findings highlight the varying performance between human expertise and AI, and the impact of the new atlas.
| Variable / Metric | Experienced Practitioners (Kappa) | Novice Practitioners (Kappa) |
|---|---|---|
| Articular Face | Good (0.7-0.8) | Poor to Moderate (0.5-0.6) |
| Irregular Porosity | Good (0.6-0.8) | Poor (0.1-0.2) |
| Upper Symphysial Extremity | Good (0.6-0.7) | Moderate (0.2-0.5) |
| Bony Nodule | Good (0.6-0.7) | Poor (0.0-0.3) |
| Lower Symphysial Extremity | Good to Very Good (0.7-0.9) | Moderate to Good (0.7-0.8) |
| Dorsal Groove (New Variable) | Good (0.6-0.7) | Poor (0.1-0.3) |
| Dorsal Plateau | Good (0.6-0.7) | Poor (0.1-0.2) |
| Ventral Bevel | Poor (0.3-0.4) | Poor (0.1-0.3) |
| Ventral Margin | Good (0.7-0.8) | Poor (0.1-0.4) |
| Todd Phase Assignment (Overall) | Good (0.6-0.7) | Moderate (0.5-0.6) |
| Conclusion: Experienced practitioners demonstrate significantly higher inter-observer agreement for component labeling and phase assignment compared to novices, except for 'Ventral Bevel', which remains challenging. This underscores the importance of expertise in traditional methods. | ||
77%
Highest accuracy achieved by Experienced Practitioner 2 using macroscopic observation (77%), which slightly outperformed the AI (69% to 73% depending on training data). This highlights that while AI is good, expert human observation can still be superior in specific cases.
Implications
The study's implications for forensic anthropology and AI integration.
Case Study: Enhancing Forensic Anthropology Workflows
Scenario: A forensic anthropology lab frequently performs age estimations from skeletal remains. Traditional methods are highly dependent on the experience of the practitioner, leading to inconsistencies and extended training periods for new staff.
Challenge: Maintaining high accuracy and inter-observer reliability across all staff, especially for intermediate age ranges, and reducing the time required for manual analysis.
Solution: Implementing the new AI-based method, trained using labels from experienced practitioners, combined with the proposed atlas. This provides a semi-automatic system that guides component labeling and offers a consistent age-at-death estimate.
Outcome: Improved consistency in age estimations, reduced training overhead for novice practitioners (as AI provides comparable results regardless of their experience level), and potential for faster processing of remains. The atlas facilitates more objective component identification, although expert validation remains crucial for edge cases.
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