Table 3.
Summarized selected traditional and novel approaches for age estimation compared to the present study. AAD: Age at Death; LA: Living Age; CBCT: Cone Beam Computed Tomography; DXA: Dual-Energy X-Ray Absorptiometry
| Ref | Skeletal element | Task | Study type and approach | Analysis | Results and performance | |
|---|---|---|---|---|---|---|
| Traditional approaches | [1] | Pubic symphysis | AAD |
Dry bone; Qualitative (macroscopic) evaluation |
Linear Regression on original sample | Classification into 1 of 6 phases corresponding to age ranges of approximately 10–50 years (age ranges increasing with increasing phase) |
| [3] | Auricular surface | AAD |
Dry bone; Qualitative (macroscopic) evaluation |
Component scoring system, Linear Regression for composite score and age | Composite scores corresponding to 7 age stages with age ranges of 3 to approximately 50 years (age ranges increasing with increasing score) | |
| [4] | Sternal end of 4th rib | AAD |
Dry bone; Qualitative (macroscopic) evaluation |
Component scoring system, Cross-tabulation analysis | Composite scores corresponding to age ranges of up to approximately 40 years (age ranges increasing with increasing score) | |
| Novel approaches | Present study | Proximal femur | AAD |
PMCT; Quantitative; Automatic Segmentation; Radiomics analysis |
Random Forest Regression | Best performing models producing MAE of approximately 10.8 years and RMSE of approximately 12.8 years |
| [9] | Proximal femur | AAD |
Plain radiographs of dry bone; Quantitative; Manual Segmentation; Appearance-based model through eigenvectors |
Principal Component Analysis, K-Nearest Neighbor Search | MAE of 13.47 years in pooled sample, 11.08 years in male sample with RMSE of 14.06 years | |
| [68] | Proximal femur | AAD |
Plain radiographs of dry bone; Quantitative; Sum of pixel values for 5 manually selected ROIs |
Linear Regression Analysis | RMSE of 11.85 years for males, 12.56 years for females, and 12.67 years for the pooled sample | |
| [44] | Pelvis | LA |
Clinical CT; Quantitative; Semi-automatic Segmentation; Radiomics analysis |
Linear Regression Analysis | MAE of 8.025 and 8.7 years in training and testing samples respectively | |
| [46] | Mandibular condyles | LA |
Clinical CBCT; Quantitative, Semi-automatic segmentation; Radiomics Analysis |
Random Forest Classification analysis | Classification accuracy ranging between 72.22–100% | |
| [69] | Cranium and femur | AAD |
DXA of dry bone; Quantitative; Bone Mineral Density |
Loess Regression Analysis | RMSE of 13 years |