# Assessment of Bone Aging--A Comparison of Different Methods for Evaluating Bone Tissue
## 1 Introduction  
Bone age assessment has long served as a cornerstone of clinical evaluation in pediatric endocrinology, orthopedics, and forensic medicine. Traditional approaches rely heavily on standardized methods such as the Greulich-Pyle (GP) atlas and the Tanner-Whitehouse(TW) scoring systems, both of which focus on hand and wrist radiographs of growing children. While these methods remain widely used, they are often limited by subjectivity, inter-rater variability, and inapplicability beyond puberty [1; 2]. As such, their relevance diminishes considerably in adult populations, where skeletal maturity precludes the use of classic growth markers. Attempts to assess age based on the analysis of X-ray images have already been undertaken, mainly in the context of forensic medicine. [PERSON] estimated the chronological age based on annealing of pelvic X-rays. However, the study was designed to show that image segmentation improves estimation accuracy and was limited to cases aged 11-21 years [3]. Other studies have mainly focused on the use of deep learning and artificial intelligence methods in age assessments based on X-ray images [4; 5]. Recent advances in artificial intelligence (AI) and image processing have reinvigorated the field, offering new methods for bone age estimation across a broader demographic spectrum. Most of these developments, however, continue to concentrate on hand X-rays and pediatric datasets. For instance, [PERSON] et al. [6] proposed a fully automated system for age assessment from infancy through adolescence, and [PERSON] et al. [7] introduced a global-local convolutional neural network tailored for hand radiographs, showing excellent accuracy in growing individuals. [PERSON] et al. [8] further expanded this frontier using a transformer-based architecture to refine attention on skeletal landmarks. Despite these technical improvements, they offer limited applicability for adult or elderly subjects, especially when radiographic landmarks associated with skeletal growth have fused. In parallel, meta-analyses such as those by [PERSON] et al. [9] have underscored the urgent need for validated methodologies that extend beyond pediatric applications, particularly in forensic and medico-legal settings, where adult age estimation is increasingly required. [PERSON] et al. [10] emphasize that in living individuals, especially adults, there is no universally accepted or standardized radiographic method for chronological age estimation--highlighting the demand for new anatomical targets and analytical strategies. In response to these limitations, our work proposes a novel approach: utilizing pelvic radiographs (both CR--computed radiography--and DR--digital radiography--modalities) for age estimation based on trabecular bone texture features. The pelvic skeleton offers a promising alternative, as it retains microarchitectural changes related to bone remodeling, mineral density, and degenerative processes across the lifespan. This approach is grounded in our previous findings, which identified significant correlations between patient age and textural features extracted from the femoral head and acetabular regions [11; 12]. Most notably, autoregressive texture descriptors--particularly ARM Theta 1--showed robust and repeatable correlations with age (up to r = 0.72 in selected subgroups), highlighting the sensitivity of pelvic trabecular architecture to chronological aging. The biological and skeletal ages of bones usually correlate with chronological age. This fact can be used in forensic medicine studies [13]. However, certain diseases, such as parathyroid dysfunction or kidney disease, can significantly impact bone health [14; 15]. This, in turn, means that chronological age may not correspond to the biological state of the bone. This knowledge can be invaluable, for example, in planning alloplasty and selecting the appropriate type of implant [16]. Importantly, this pelvic-based method also holds promise for broader clinical and forensic applications.