## 4 Discussion
Higher correlation values associated with condylar flattening and erosion suggest higher linear interdependence between pixel intensities, which reflects changes in bone density and organization. As the degenerative process progresses, the body attempts to adapt by altering the trabecular architecture and increasing bone density in the affected regions [30]. This adaptive response manifests as higher correlation values in TA, indicating a more organized and interdependent distribution of pixel intensities. Furthermore, the observed changes in the contrast, sum of squares, and entropy parameters in the cases of condylar erosion reflect alterations in the heterogeneity and complexity of the medullary bone structure [31; 32]. These findings align with the expected pathophysiological changes associated with advanced degenerative processes, such as bone remodeling, trabecular disruption, and the formation of erosive lesions [33]. This study's findings underscore the complementary nature of CT and MRI in assessing TMJ degeneration through TA. While CT excels in capturing changes related to cortical bone integrity and density, MRI provides valuable insights into alterations within the medullary bone and surrounding soft tissues. By combining the strengths of both imaging modalities, TA offers a comprehensive evaluation of the degenerative process by encompassing cortical and medullary bone changes, including associated soft tissue alterations. This multimodal approach enhances diagnostic accuracy and facilitates a more holistic understanding of the disease's progression. The ability to detect early degenerative changes within TMJ condyles through TA holds significant clinical implications. Early intervention is essential in mitigating the progression of TMJ degeneration and preventing irreversible damage to the joint [34; 35]. By identifying subtle alterations in the medullary bone structure, TA can serve as a valuable tool for clinicians in enabling them to initiate appropriate treatment strategies at the earliest possible stage. This proactive approach may include conservative measures, such as lifestyle modifications, physical therapy, or pharmacological interventions, aimed at slowing or halting the degenerative process. Moreover, the quantitative nature of TA parameters opens the door for personalized treatment planning and monitoring. By tracking changes in these parameters over time, clinicians can tailor treatment regimens to individual patient needs, thus optimizing outcomes and minimizing potential complications. As the field of medical imaging continues to evolve, the integration of artificial intelligence (AI) and machine learning (ML) techniques holds immense potential for further enhancing the diagnostic capabilities of TA [36]. By leveraging large datasets of TA parameters and clinical outcomes, AI algorithms can be developed to recognize intricate patterns and correlations that may be imperceptible to human observers [37]. These algorithms can then assist in the automated detection, classification, and prognosis of TMJ degenerative conditions by streamlining the diagnostic process and reducing the potential for human error. Additionally, the incorporation of ML techniques can facilitate the development of predictive models to enable clinicians to anticipate the progression of degenerative changes and tailor treatment strategies accordingly. This proactive approach can significantly improve patient outcomes and quality of life. Our study's methodology aligns with and builds upon recent advancements in TA for TMJ evaluation. [PERSON] et al. [38] applied TA to MRI to identify changes in TMJ discs affected by effusion, utilizing similar GLCM parameters in MaZda software. Their findings of distinct texture patterns in effusion-affected discs support the potential of TA in detecting subtle TMJ changes. In a related study, [PERSON] et al.