Age Estimation of the Cervical Vertebrae Region Using Deep Learning
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View abstract on PubMed
Summary
This summary is machine-generated.Deep learning models using cervical vertebrae region (SR) and cervical vertebrae (CV) representations from lateral cephalometric radiographs (LCR) significantly improve age estimation accuracy. The SR mode, incorporating surrounding soft tissues, demonstrated superior performance across a wide age range (4-40 years).
Area Of Science
- Medical Imaging Analysis
- Deep Learning
- Forensic Anthropology
- Radiographic Age Estimation
Background
- Traditional methods struggle to detect subtle age-related bone changes in adulthood.
- Deep learning shows promise for medical image-based age estimation.
- Cervical vertebrae in lateral cephalometric radiographs (LCR) are valuable for age assessment.
Purpose Of The Study
- To systematically investigate the impact of different cervical vertebral representations on age estimation accuracy.
- To compare the performance of Contour (C), Mask (M), Cervical Vertebrae (CV), and Cervical Vertebrae Region (SR) input modes.
- To evaluate age estimation across different age groups (4-40 years) using deep learning.
Main Methods
- Developed four distinct input modes (C, M, CV, SR) for deep learning models.
- Utilized a large-scale dataset of 20,174 LCRs from subjects aged 4-40 years.
- Evaluated performance using Mean Absolute Error (MAE), analyzing individual and combined vertebrae.
Main Results
- The Cervical Vertebrae Region (SR) mode achieved the lowest overall MAE, outperforming CV, C, and M modes.
- SR and CV modes maintained MAE below 10 years for the 26-40 age group, unlike C and M modes.
- Combining vertebrae improved accuracy, with continuous combinations (e.g., C1-2 + C3) showing better results than discontinuous ones.
Conclusions
- Incorporating peripheral soft tissue and comprehensive vertebral context (SR mode) is crucial for accurate age estimation.
- The SR mode offers superior performance, especially for older age groups, compared to methods focusing solely on bone structure.
- Deep learning models leveraging advanced vertebral representations can effectively estimate age from LCRs across a broad spectrum.
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