# Assessment of Bone Aging--A Comparison of Different Methods for Evaluating Bone Tissue
## 4 Discussion
Moreover, in the case of forensic medicine, we do not have access to knowledge about a person's medical history. Our study supports the growing body of evidence that bone texture features can serve as reliable indicators for biological age estimation, extending the scope beyond traditional hand-based radiographs. Most existing models target pediatric populations using left-hand X-rays; however, our approach, based on pelvic X-rays (both CR and DR modalities), demonstrates that adult bone structures, especially in the pelvis, also encode discernible age-related patterns. Using a texture-based feature analysis of 480 pelvic X-rays, we observed a maximum correlation around 0.5 between age and the ARM Theta 1 parameter, particularly in the femoral head (R05, L05). This suggests a significant correlation between trabecular architecture and chronological age. Our results are particularly relevant for adult and elderly populations, where traditional hand-based methods lose diagnostic value due to skeletal maturity. This directly addresses a limitation outlined by [PERSON] et al. [2], who critically reviewed bone age assessment techniques and noted that the most widely used methods (e.g., Greulich-Pyle and Tanner-Whitehouse) become ineffective or ambiguous in adults. They highlighted the lack of standardized approaches for age estimation in skeletally mature individuals--an area where our pelvic X-ray analysis provides new potential. Furthermore, our method offers interpretability, a feature that is increasingly lacking in modern AI-based systems. While black-box models, such as those presented by [PERSON] et al. [31] and [PERSON] et al. [32], leverage deep features from epiphyseal or articular regions, they often obscure the anatomical rationale behind their predictions. In contrast, our texture-parameter-based model, which focuses on clearly defined pelvic ROIs, enhances transparency and clinical relevance. This interpretability is particularly valuable in legal or forensic contexts, where decision accountability is essential--a concern raised by [PERSON] [1] in his discussion of bone age in medico-legal assessments. Interestingly, [PERSON] [1] also emphasized the variation in bone maturation patterns across anatomical regions and patient backgrounds. Our findings echo this by demonstrating that ROI selection and preprocessing had differing effects on age correlation strength. Notably, we observed that narrowing the ROI to specific regions (e.g., R05, L05) significantly improved model performance. These findings diverge from prevailing trends in the literature, where deep learning models are the dominant approach. For instance, [PERSON] et al. [33] developed CNNs robust to real-world image noise in pediatric assessments, and [PERSON] et al. [34], in a comprehensive meta-analysis, confirmed the dominance of ML techniques--particularly in children. However, they also highlighted wide methodological variability and a gap in adult-specific models, reinforcing the importance of approaches like ours. While separating by sex did not consistently increase correlation, focusing on key anatomical structures yielded more robust results. This supports the idea advanced by [PERSON] et al. [31] that anatomical specificity in input data is crucial for improving bone age prediction, especially when using neural networks. Postmenopausal status may have influenced bone aging in females, but the change in bone quality that progresses with age is also observed in males [35]. In addition, our consistent identification of ARM Theta 1 as a top-performing feature aligns with the broader push toward texture-aware AI models. This may serve as a bridge between interpretable radiomic analysis and more opaque deep learning systems. Future work may involve combining these insights with CNN frameworks and activation map visualizations (e.g., Grad-CAM [36]), as proposed by [PERSON] [33], to understand better which anatomical features drive model predictions.