# Correlation of Bone Textural Parameters with Age in the Context of Orthopedic X-ray Studies
## 4 Discussion
Although, for many years, the reference for diagnostic imaging of bone in humans was the interpretation of a radiologist [25], who is a specialist in diagnostic imaging, the ability of the observer to detect image details does not necessarily reflect the amount of information present in the image [24]. Therefore, many attempts have been made to evaluate bone tissue structure, which is especially susceptible to textural analysis [39]. The idea of using textural analysis for the evaluation of human tissue visualized in diagnostic imaging techniques is not new. It has been implemented for brain tissue [40; 41; 42], breast [43], and even muscle [44]. In the context of bone-based analysis, textural parameters are used, e.g., for the prediction of incident radiographic hip osteoarthritis [45], age-texture correlation analysis [46; 47], and association with bone mineral density [48] and bone quality [49]. Contemporary emphasis must be placed on the role of artificial intelligence (AI), in particular deep learning (DL), in the broad field of bone analysis. It is a revolutionary tool that allows researchers to the generation of radiological reports for fractures of the proximal femur [50] and detects fractures directly on the image from different modalities [51; 52; 53]. AI-based methods have also demonstrated the potential to predict a patient's age or gender based on images of the bone [54; 55]. Furthermore, neural networks can be a valuable support for bone segmentation [56; 57] and the detection of a range of bone diseases [58; 59]. The textural analysis presents certain benefits when compared to AI. In our theoretical overview, we elucidated the biological basis for changes in bone texture and demonstrated that there are certain specific parameters that are correlated with age. Textual analysis is more suitable for interpretation and, in this particular study, allows us to conclude that the microstructure of bone changes with age, which is accentuated in the images as an increase in the homogeneity and uniformity of pixel values. Furthermore, textural analysis can derive meaningful insights from smaller datasets compared to deep learning. Optional research may be conducted on bone corticalization [60]. This is a relatively new approach as, to the best of our knowledge, there are no similar approaches presented, excluding the work of our team [61] and the work of [PERSON] [62], who evaluated age among different parameters with the use of deep learning methods; therefore, the approach was very different. Existing works where textures are used for bone assessment are focused on the evaluation of the presence of osteoporosis [63; 64]. Textural bone assessment for osteoporosis evaluation was provided on different parts of the skeletal system, such as the jaw [65] and head [66], and with the use of different modalities as CT and MR [67]. Studies on bone mineralization were also evaluated in children [68]. However, bone mineral density was studied already with the use of textural methods, and works where age assessment was performed are lacking; therefore, we assume that our study will have an important contribution to the current literature status. This study also has several limitations that merit discussion. First, the image acquisition was collected as digitalized images, which may have impacted the quality of the obtained images. In addition, a subset of the acquired images contained endoprostheses, resulting in a lower number of marked regions of interest (ROIs) and possibly affecting the overall distribution of the data. Another limitation of our study was the use of a broad demographic group with the inclusion of extremes of participants' age (youngest and oldest). The demographic is also limited to Caucasian origin with an extended group of participants aged 25-91; however, most of the examined group are above the sixth decade of life. Differences in bone mineral composition may influence the presented results.