Synthetic proximal femur models have been widely employed as reliable surrogates to replicate the complex geometry and mechanical behavior of the human proximal femur. By closely replicating the anatomical morphology and mechanical properties of the femur, these models ensure repeatability and reliability in quantitative experiments and numerical analyses [1–3]. Moreover, they overcome the ethical and technical limitations associated with the use of actual human bone specimens and have served as surrogate models in biomechanical research to investigate the mechanical response of the proximal femur. Importantly, because the loads applied to the femur are difficult to predict, synthetic proximal femur models provide insights into the given loading conditions and the resulting bone structures, enabling the analysis of the relationship between the applied loads and bone morphology [4–7].

The human skeletal system adapts to externally applied mechanical loads by altering the bone structure and density, ultimately forming an optimized structural configuration [8–11]. Among the bones in the human body, the femur most clearly demonstrates such adaptability as it is the largest structure supporting the body weight and is responsible for effectively distributing the complex loads generated during daily activities [12–14]. This adaptive process is well explained by Wolff’s law [15], which states that bones remodel themselves under the action of repetitive mechanical stimuli to maintain structural stability. Understanding this adaptability is essential for investigating femoral biomechanics, and such processes can be virtually simulated using synthetic proximal femur models [16].

The proximal femur is directly connected to the hip joint and is continuously exposed to loads of varying directions and magnitudes [17–19]. This complex loading environment imposes diverse mechanical stimuli across the entire femur, and when combined with its intrinsic anatomical and structural characteristics, leads to region-specific differences in load adaptability [12,20,21]. These differences are fundamentally associated with the characteristic triangular structural geometry of the proximal femur, in which the superior, medial, and lateral cortical walls converge in the coronal plane to form a high-modulus framework [22,23]. This triangular configuration not only enhances the overall femoral stability by reducing bending moments, balancing shear forces, and improving load transfer efficiency but also serves as a key factor in inducing distinct patterns of load adaptability across different regions. However, attempts to quantitatively evaluate such region-specific adaptations and structural optimality remain limited because most existing studies have primarily focused on the global load distribution or localized stress concentrations. In fact, many prior studies have assessed the load adaptability of the proximal femur only at the whole-structure level or restricted their analyses to limited regions such as the femoral head and neck. Such approaches are insufficient to systematically reveal the differences in load adaptability across regions [24–28]. Therefore, a precise quantitative evaluation at the region-of-interest (ROI) level is required to better understand the region-specific structural adaptations of the proximal femur.

An ROI-level evaluation becomes particularly important considering that the structural responses of the femur may vary depending on the loading conditions derived from daily activities. In fact, only a few studies have systematically analyzed the differences in the load distribution or quantitative variations at the ROI level under representative loading scenarios such as one-legged stance, adduction, and abduction. Moreover, there is a need to clarify the structural adaptability of the proximal femur to loading under complex conditions in which the hip contact forces (HCFs) and muscle forces (MFs) of different origins act simultaneously [29]. Existing studies have often relied on simplified load models based on the foundational work by Beaupré et al. or have averaged the loads under specific motions; however, these approaches have not been extended to region-specific adaptability assessments [27,30–33]. Therefore, a more precise analysis of the load adaptability of the proximal femur would require a new approach that incorporates a regional-level quantitative assessment and diverse loading conditions.

This study aimed to quantitatively evaluate the load adaptability and structural optimality of the proximal femur across individual ROIs using synthetic proximal femur models. To this end, three synthetic proximal femur models were generated under representative loading scenarios: one-legged stance, adduction, and abduction. Variations in the stress distribution and strain energy density (SED) were analyzed by altering the applied angles of the HCFs and MFs. Moreover, the effects of model resolution and ROI size on the predictive accuracy of structural optimality were examined, enabling quantitative comparisons of the structural adaptability of the proximal femur under different model resolutions, ROI sizes, and loading conditions. Through a regional conformity analysis, this study aimed to elucidate the functional structural adaptability of the proximal femur and provide quantitative data and evaluation indices that may contribute to customized implant and scaffold designs, fracture prevention, and rehabilitation strategies.

The load adaptability and region-specific structural optimality of the proximal femur were investigated by quantitatively analyzing the load dependency of each ROI under various external loading conditions and identifying the dominant load condition through a systematic three-step methodology, as outlined in Fig. 1. First, a synthetic proximal femur model was generated for the load adaptability analysis (Section 2.1). Second, a finite element (FE) structural analysis was performed by varying the loading angles of HCFs and MFs (Section 2.2). Finally, the proximal femur was divided into grids, and the load adaptability of different ROIs was quantitatively assessed to evaluate regional structural optimality (Section 2.3).

By comparing the conformity between the reference and predicted loading angles across the ROIs, we found clear differences in the load adaptability of the proximal femur depending on the ROI. Tables 1 and 2 present the conformity indices with respect to variations in the HCF and MF angles, respectively, indicating the degree of load adaptability. In the high-resolution model (ROI size 4.8 mm) under HCF-one-legged stance, the femoral head exhibited the highest conformity (98.25%), indicating excellent structural adaptability. The femoral neck showed the lowest conformity, with an average of 93.80%; in particular, cortical bone–dominant ROI 55 exhibited the lowest conformity of 73.08%. The intertrochanteric region and the greater trochanter maintained high conformity, with errors of only 2.33% and 2.20%, respectively. By contrast, under the abduction model, the greater trochanter exhibited the highest conformity (92.04%), whereas the femoral head (77.45%), femoral neck (79.50%), and intertrochanteric region (78.14%) all showed similar, lower levels of adaptability. Under the adduction model, conformity values were approximately 90% across all regions, indicating little regional variation. These results demonstrate that the load adaptability of the proximal femur varies depending on both anatomical location and loading condition, with certain regions responding more sensitively to specific load cases.

In terms of the MF results, analysis based on subdivision into 4.8 mm × 4.8 mm ROIs revealed that the greater trochanter exhibited the highest adaptability with a conformity of 97.41% (error 2.59%). The intertrochanteric region, femoral neck, and femoral head followed with conformity values of 96.17%, 87.19%, and 60.00%, respectively. Thus, the order of conformity from highest to lowest was greater trochanter, intertrochanteric region, femoral neck, femoral head. A Similar ranking was observed in the abduction and adduction models with the same ROI size. These findings indicate that the load adaptability to MF in the proximal femur is highest in ROIs closest to the applied force and lowest in those located farther away.

A comparison of the load conformity with respect to the model resolution revealed that the LR model exhibited an overall lower conformity than the HR model. Figs. 8 and 9 show histograms of prediction errors across ROIs under HCF and MF one-legged stance models, respectively, indicating that the distribution shifted upward in the low-resolution model, corresponding to increased error (results for adduction and abduction models are presented in Figs. A1–A4). As shown in Table 3, for HCF-one-legged (ROI 4.8 mm), conformity in the femoral head decreased from 98.25% to 76.07% (−22.18%p), whereas the femoral neck, greater trochanter, and intertrochanteric region decreased by 18.30%p, 9.54%p, and 2.54%p, respectively. Under HCF-abduction, the femoral neck decreased by 18.99%p, whereas under HCF-adduction the femoral head and greater trochanter decreased by 14.95%p and 5.48%p, respectively. In MF-one-legged (ROI 4.8 mm), the femoral neck decreased from 87.19% to 60.62%, corresponding to a reduction of 26.57%p (Table 2). Overall, conformity in the low-resolution model decreased by between 2.54%p and 26.57%p, with the femoral head and femoral neck identified as the regions most sensitive to resolution changes.

When comparing prediction accuracy according to loading condition, conformity was highest in the one-legged stance, followed by adduction, and lowest in abduction (Table 1). In the high-resolution HCF-one-legged stance model, the femoral head and greater trochanter exhibited the highest conformity at 98.25% and 97.80%, respectively. Under HCF-adduction, all regions showed conformity values of approximately 90%, slightly lower than in the one-legged stance, but still relatively high. In contrast, HCF-abduction exhibited the lowest conformity overall, with the femoral neck and greater trochanter at 71.75% and 68.88%, respectively. Under MF loading, conformity was also highest in the one-legged stance model (average approximately 94%), followed by the adduction model (approximately 90%) and the abduction model (approximately 88%) (Table 2). In summary, across all loading conditions, conformity was consistently highest in the one-legged stance model and lowest in the abduction model.

When comparing conformity according to ROI size, larger ROIs consistently yielded higher conformity across all loading conditions (Table 1). In HCF-one-legged, femoral neck conformity increased from 93.49% at 4.8 mm to 95.82% at 9.6 mm and 96.81% at 14.4 mm. In HCF-adduction, femoral neck conformity increased from 92.96% (4.8 mm) to 93.08% (9.6 mm) and 93.41% (14.4 mm). In HCF-abduction, femoral neck conformity was lowest at 71.75% (4.8 mm) but increased to 75.05% (9.6 mm) and 84.51% (14.4 mm). Thus, conformity generally improved as ROI size increased, with the femoral neck—initially showing the lowest values at small ROIs—also exhibiting higher conformity at larger ROIs. In contrast, the low-resolution model did not exhibit consistent improvement with increasing ROI size. In HCF-one-legged, femoral neck conformity increased from 75.19% (4.8 mm) to 82.09% (9.6 mm), but decreased again to 80.11% at 14.4 mm. In HCF-abduction, conformity was lowest at 4.8 mm (71.75%) but steadily increased to 83.49% (9.6 mm) and 85.72% (14.4 mm). Therefore, unlike the high-resolution model, the low-resolution model showed irregular patterns in conformity with ROI size, while maintaining lower absolute values across all conditions (Table 1).

Numerical robustness of the conformity index Cθ was evaluated with respect to model resolution and ROI size using the metrics defined in Section 2.4. Resolution sensitivity, expressed as |ΔCθ|=|Cθ,HR−Cθ,LR|, revealed the largest worst-case deviation in the femoral neck (max |ΔCθ| = 24.50% for HCF and 33.40% for MF), whereas the intertrochanteric region exhibited consistently smaller deviations across loading conditions (Table 3). ROI-size robustness was assessed using the normalized range NR (%), which quantifies the variability of Cθ across the three ROI sizes. The largest variability was observed in the femoral neck under HCF conditions (LR NRmax = 26.37%), whereas the femoral head showed the largest average variability under MF conditions (HR NRmean = 10.46%) (Table 4). Overall, these robustness metrics consistently indicate that the femoral neck is the most resolution- and ROI-size-sensitive region, whereas the intertrochanteric region exhibits comparatively stable numerical behavior across modeling conditions. These results indicate that prediction stability is better interpreted in terms of variability with respect to ROI size rather than a strictly monotonic increase of Cθ with increasing ROI size. The interaction patterns across resolution, ROI size, and loading condition are illustrated in Appendix B.

We first quantitatively analyzed the structural response characteristics and load adaptability of the proximal femur. The results showed that, even under identical loading conditions, the femoral head and greater trochanter exhibited high conformity, whereas the femoral neck showed low conformity, indicating region-specific differences with respect to local anatomical features. Furthermore, a reduction in the model resolution significantly affected the predictive accuracy, with the femoral neck being the most sensitive region, while the intertrochanteric area remained relatively stable. Depending on the loading conditions, a high conformity was maintained under localized concentrated loads; however, the predictive accuracy decreased when stresses were more widely distributed. The ROI size was another important factor influencing interregional variability. Small ROIs were advantageous for capturing local responses, whereas large ROIs were more effective in ensuring stability and reproducibility. These findings are consistent with previous results—the anatomical structure, model resolution, loading conditions, and ROI size influence the reliability of FEM-based analyses. Moreover, this study quantitatively demonstrated that these four factors were the key determinants of the structural adaptability of the proximal femur under loading.

Our quantitative analysis confirmed that the structural responsiveness varied across ROIs within the proximal femur, even under identical loading conditions. In the present two-dimensional analysis, the conformity results are interpreted primarily as comparative ROI-wise trends; therefore, the following results are discussed in terms of regional differences under controlled changes in loading direction [48]. Based on the HR model with an ROI size of 4.8 mm under the one-legged stance condition, the femoral head exhibited the highest conformity, with a mean value of 98.25%, whereas the greater trochanter and intertrochanter maintained high conformity values of 97.80% and 97.67%, respectively. In contrast, the femoral neck showed the lowest mean conformity of 93.49%, and particularly in the cortical bone-dominant regions, the conformity dropped to as low as 73.08%. These differences indicate that the femoral head, as a direct load-transfer pathway of joint compressive forces, has high structural adaptability to the HCF [49–51], whereas the femoral neck, which is least influenced by the HCF, demonstrated lower structural adaptability [20,52,53]. This suggests that while the trabeculae in the femoral neck contribute to reinforcement and stress distribution, they are not the primary load-bearing structures [54,55] and that conformity is primarily governed by the cortical bone strength in this region. Under MF conditions, the greater trochanter showed the highest conformity, with a mean value of 97.41%, followed by the intertrochanter at 96.17%. This finding is consistent with those of previous studies [20,52,53] that reported that the greater trochanter, as a major muscle attachment site, directly responds to muscle loading. In the present study, this influence was further observed to extend into the intertrochanteric region. By contrast, the femoral head and neck exhibited relatively lower conformity under MF conditions, suggesting that these regions are more strongly influenced by joint compressive loads than by MFs. These regional differences align with recent structural models proposing that the proximal femur has triangular and arch-based mechanical characteristics [22,23]. The triangular structure formed by the superior, medial, and lateral cortical walls efficiently distributes the joint compressive loads, whereas the trabecular alignment along the arch-shaped framework provides mechanical stability by alternately supporting compressive and tensile forces. The high conformity observed in the femoral head and greater trochanter, the low conformity in the femoral neck, and the intermediate conformity in the intertrochanter quantitatively demonstrate how this arch-based load transfer mechanism manifests in actual structural responses. In conclusion, the proximal femur structurally adapts to mechanical loading; however, the femoral responses to HCFs and MFs are distinctly differentiated in terms of the local anatomy and loading patterns. SED has frequently been adopted as a mechanical stimulus in computational studies of bone adaptation and remodeling. Classical mechanoregulation theories propose that trabecular architecture evolves in response to the mechanical environment so that the internal mechanical stimulus approaches a regulated state [45,46]. Based on this concept, several computational studies have used SED-based measures to investigate trabecular bone adaptation and load-driven structural responses [48,56,57]. From this perspective, the conformity index used in the present study can be interpreted as a regional descriptor of how uniformly mechanical energy is distributed within a trabecular structure under different loading orientations. Accordingly, the CI reflects the relative adaptability of trabecular architecture to different loading directions rather than representing a universal criterion of structural optimality.

This study quantitatively confirmed that changes in the load conformity due to differences in model resolution varied across ROIs in the femur. Compared with the HR model (50 μm), the LR model (600 μm) exhibited a predominant tendency toward reduced conformity, with minor exceptions in specific combinations of ROI, loading condition, and ROI size, as shown in Figs. 7 and 8. Based on regional conformity values, in the HCF scenario, changes ranged from a minimum increase of 0.12% in the intertrochanteric ROI of the one-legged stance model with 9.6 mm ROIs to a maximum decrease of 24.5% in the femoral neck ROI of the abduction model with 9.6 mm ROIs. A similar trend was observed in the MF scenario, where under one-legged stance conditions, the femoral neck ROI showed the highest reduction in conformity, up to 33.4%. These results demonstrate that the femoral neck is the most sensitive ROI to model resolution changes in both the HCF and MF models. In contrast, the intertrochanteric ROI maintained a relatively stable conformity despite the resolution differences, showing the lowest sensitivity. These findings are consistent with those of previous studies [58] that reported that a lower model resolution underestimates or distorts the stress concentration and strain distribution, thereby markedly reducing the predictive accuracy of the load response. Furthermore, by capturing the numerical differences in the conformity, this study quantitatively demonstrated that the femoral neck ROI was the most resolution-dependent, whereas the intertrochanteric ROI maintained a relatively stable conformity.

The predictive accuracy of structural optimality under different loading conditions improved when the applied load was more localized but reduced when it was more widely distributed. Under the one-legged stance condition, the femoral head and femoral neck exhibited conformity values of approximately 98.25% and 93.49%, respectively, indicating a very high accuracy. As shown in Fig. 10a, this results from the concentrated transmission of the hip joint contact load to the femoral head and neck, leading to locally high conformity. In contrast, under the abduction condition, the conformities of the femoral neck and greater trochanter decreased to approximately 71.75% and 68.88%, respectively, falling below 70%, as illustrated in Fig. 10b, where stresses are more widely distributed in the intertrochanteric region and greater trochanter, leading to reduced local conformity. The adduction condition exhibited an intermediate trend: as shown in Fig. 10c, the femoral head maintained a conformity of 91.89%, while the other regions also maintained a conformity of 90%, which was lower than that under the one-legged stance condition. A similar trend was observed under MF loading, where the one-legged stance model showed the highest accuracy with a conformity of 97.41% in the greater trochanter, whereas the adduction model showed the lowest at 85.91% (Fig. 10d–f). These findings suggest that conformity serves as a key indicator in explaining structural responses; more localized loading results in higher conformity, whereas more distributed loading results in lower conformity. This is consistent with previous studies that reported that under complex and distributed loading conditions, such as sideways falls [59], the lateral femoral neck exhibited prediction errors of approximately 30%, the highest among the regions. In other words, simple localized loading conditions preserve the predictive accuracy, whereas multiaxial and distributed loadings, such as falls, can cause a sharp decline in the conformity of specific regions. Therefore, this study quantitatively demonstrated that the differences in conformity under varying loading conditions are not merely numerical changes but represent fundamental alterations in the structural response patterns of the proximal femur depending on whether the applied load is localized or distributed.

A comparison of the predictive accuracy across different ROI sizes revealed that, overall, larger ROIs yielded higher conformity and improved the stability of the results. Smaller ROIs (e.g., 4.8 mm) often included anatomical boundaries, such as the cortical–trabecular junction or regions with large structural variations, where local element-level differences were strongly reflected. Consequently, conformity showed greater variability and lower mean values. For example, under the one-legged stance condition, the femoral neck with a 4.8 mm ROI exhibited a conformity of 93.49%, which was lower than those of the other regions. In contrast, when the ROI size was increased to 9.6 and 14.4 mm, broader anatomical structures were encompassed, averaging out microstructural heterogeneity. Consequently, conformity was maintained more stably, and interregional deviations decreased to 4.18% and 3.19%, respectively. This suggests that while small ROIs are advantageous for detecting local variations, larger ROIs are more suitable for ensuring consistency and reliability in the overall load adaptability assessment. These findings are consistent with computed tomography quality control studies [60], which reported that maintaining an ROI above a certain size is essential for accurate noise measurements, highlighting that the ROI size is not a simple technical parameter but a crucial factor determining the reproducibility and reliability of the results. Previous studies have reported that the selected ROI volume can influence trabecular microarchitectural parameters. For example, Wen et al. [61] showed that insufficient ROI volume in the femoral head can significantly affect measured trabecular parameters. In addition, proximal femur trabecular studies have commonly employed ROIs with dimensions of several millimeters; for instance, Munemoto et al. [62] defined cylindrical ROIs with a diameter of approximately 5 mm in the femoral head for trabecular microstructure analysis. Consistent with these findings, our previous trabecular reconstruction study based on topology optimization [63] also showed that decreasing ROI size increased variability in trabecular morphometric indices, particularly in the femoral neck region. Taken together with the present results, these observations indicate that ROI size should be carefully considered when evaluating regional structural metrics. In particular, relatively larger ROIs may provide more stable conformity values when the objective is regional comparison rather than detection of highly localized variations. The robustness analysis further clarifies how discretization choices influence the conformity index Cθ. The metrics summarized in Tables 3 and 4 indicate that prediction stability is most appropriately interpreted through variability measures, specifically resolution sensitivity |ΔCθ| and ROI-size variability (NR), rather than through a universal monotonic increase of conformity with ROI size. This distinction is particularly relevant under MF conditions, where non-monotonic behavior of the mean Cθ with increasing ROI size can occur. Among the examined regions, the femoral neck exhibited the largest worst-case resolution sensitivity and the highest ROI-size variability under HCF conditions, suggesting that this region is most susceptible to discretization and ROI-definition effects. In contrast, the intertrochanteric region showed comparatively small |ΔCθ| and low NR values across loading conditions, indicating greater numerical stability for trend-level interpretation. Appendix Figs. A5 and A6 provide visual confirmation of these interaction patterns.

The regional conformity patterns identified in the present study indicate that structural responsiveness of the proximal femur varies across anatomical regions depending on their role in load transfer. Specifically, the femoral head and greater trochanter exhibited higher conformity under their dominant loading conditions, whereas the femoral neck showed lower conformity, suggesting that mechanical responses are region dependent rather than uniform across the bone. This interpretation is consistent with recent computational studies on bone mechanoadaptation, which have demonstrated that bone adaptation is governed by spatially heterogeneous mechanical environments. In particular, three-dimensional finite element frameworks combined with micro-CT imaging have shown that trabecular bone formation and resorption tend to occur in region-dependent patterns associated with local mechanical stimuli [56,57]. These findings indicate that local structural characteristics and load-transfer pathways strongly influence how different anatomical regions respond to mechanical loading. Recent reviews further emphasize that contemporary bone adaptation modeling increasingly incorporates three-dimensional geometry, multiaxial loading conditions, and subject-specific musculoskeletal forces to improve the physiological realism of predicted remodeling patterns [48]. Within this broader context, the ROI-based conformity analysis used in the present study can be interpreted as a controlled regional sensitivity framework that evaluates how structural responses vary across anatomical regions under systematically varied loading orientations. Although the present analysis does not explicitly simulate long-term bone remodeling processes, the observed regional conformity trends may provide baseline insight for future studies incorporating subject-specific loading conditions and fully three-dimensional adaptive remodeling models.

This study had several limitations. First, this study was performed using a two-dimensional representation of the proximal femur. Consequently, deformation and load transfer in the thickness direction are not explicitly represented, and the detailed stress distribution and load transfer pathways may differ from those predicted by fully three-dimensional models [61]. In addition, two-dimensional analyses cannot represent the full three-dimensional structural connectivity between cortical and trabecular bone [62]. Therefore, the present results should be interpreted primarily as a comparative analysis of regional conformity patterns under controlled variations in loading direction. Nevertheless, as described in Section 2.3, because the conformity index is computed consistently across ROIs under controlled variations in loading direction, the present two-dimensional framework remains meaningful for comparative, region-specific trend identification [64]. Future studies using three-dimensional models may further refine the interpretation of regional load adaptability. Second, the loading conditions were limited to HCF and MF, without reflecting more complex and multiaxial loading scenarios. Nevertheless, by focusing on the representative loading conditions, this study identified the fundamental structural response characteristics of the proximal femur, which can be extended to future studies involving gait or falls. Third, long-term bone remodeling processes or temporal adaptation to load changes were not considered; instead, this study focused on short-term structural responses. These aspects should be addressed in future studies through time-dependent simulations. Fourth, because the conformity index was defined based on trabecular bone elements, cortical bone responses were not explicitly included in the current metric. Incorporating cortical bone measures may provide additional insights, particularly in cortical-dominant regions such as the femoral neck, and represents a potential direction for future work. Fifth, the directions of hip contact force and muscle forces were varied independently in this study. Although hip joint loading in vivo results from coordinated muscle activation and joint reaction forces, the independent variation adopted here provided a controlled framework for evaluating the directional sensitivity of regional structural responses. Future studies may incorporate coupled musculoskeletal loading conditions to further investigate the influence of coordinated loading interactions on regional adaptability patterns. Despite these limitations, we quantitatively demonstrated how model resolution, loading conditions, and anatomical location interact to determine the load adaptability of the proximal femur, providing baseline data for future studies involving 3D modeling and complex loading conditions.

This study constructed multiple 2D FE models to quantitatively evaluate the structural responses and load adaptability of the proximal femur across ROIs and compared the predictive accuracy under different loading conditions (HCF, MF), model resolutions (HR: 50 μm, LR: 600 μm), and ROI sizes (4.8–14.4 mm). The analyses revealed that, even under identical loading conditions, the femoral head and greater trochanter exhibited high conformity, whereas the femoral neck showed low conformity, indicating that local anatomical characteristics play a decisive role in load adaptability. In addition, a lower resolution consistently decreased the predictive accuracy, with the femoral neck being the most sensitive region, while the intertrochanteric region remained relatively stable. Across the loading conditions, the one-legged stance condition, in which loads are more localized, produced the highest conformity, whereas conditions, such as adduction and abduction, where stresses are more widely distributed, resulted in markedly reduced predictive accuracy. Furthermore, increasing the ROI size reduced the local variability and improved the stability of the conformity, demonstrating that the ROI size is a critical factor influencing the reliability of result interpretation. These findings are consistent with previous results—the anatomy, model resolution, loading conditions, and ROI size affect the accuracy of FEM-based analyses. By quantitatively demonstrating these effects, this study provides new evidence for understanding the structural adaptability of the proximal femur. Although limited by the 2D modeling and simplified loading conditions, our findings provide foundational insights into how ROI characteristics, model resolution, and loading conditions interact to determine structural responses, thereby supporting future 3D extensions and clinically applicable studies.