## 4 Discussion
The DAE, representing the outermost part of the acetabulum where the femoral head's articulation terminates dorsally, is a more geometrically consistent landmark compared to the CrAR. The DAE's greater length and continuity make it easier to identify radiographically, even in cases of moderate DJD. The DAE remains visible along most of its length, even though it appears dorsal to the femoral neck in the VDHE view, becoming difficult to define only in extreme cases of significant abnormal bone growth on the femoral head, femoral neck, or acetabulum, or in cases of severe joint deformation [34; 35]. This robustness makes the DAE a more practical and reliable reference point for assessing femoral head coverage, particularly in the scope of DL models that rely on precise pixel-level detection to define anatomical structures through semantic segmentation. Beyond the inherent anatomical variability of the NA, additional inconsistencies result from its categorization within the FCI and Fluckiger's CHD scoring protocols. The classification relies on predefined NA thresholds, but overlapping criteria, such as cases where an NA of 105\({}^{\circ}\) or greater is classified differently depending on slight variations in joint space [3], introduce ambiguity. This lack of strict boundary distinctions, combined with subjective interpretation of anatomical features, can lead to misclassification, particularly in borderline cases. In contrast, the FHC/DAE metric offers a clear advantage by providing well-defined category boundaries, minimizing overlap and improving classification clarity. Accurate localization of anatomical landmarks on the femoral head is crucial for the automated FHC/DAE measurement system, as the precision of the FHC depends on the exact placement of the predicted keypoints. The results indicate that the FHC detection model achieved highly precise localization, with evaluation metrics such as ED, MAE, and MSE confirming minimal deviations from ground truth annotations. In medical imaging, segmentation plays a fundamental role in accurately delineating anatomical structures, facilitating precise identification and isolation of regions critical for measurement [36]. In this study, the DAE segmentation model demonstrated strong performance in acetabulum delineation, ensuring accurate identification of the DAE for FHC/DAE measurement calculations. The DS and IoU scores further validated the segmentation process, confirming a high degree of overlap with expert-annotated ground truth masks. Compared to our previous acetabulum segmentation models, the current model demonstrated improved performance, benefiting from a significantly larger training dataset. In earlier studies [22; 24], a DS of 0.93 and an IoU of 0.88 were achieved using a training set of only 138 images. By contrast, the current model, trained on 624 images, achieved a DS of 0.96 and an IoU of 0.92, reflecting a notable improvement in segmentation accuracy. Beyond our prior work, this model also outperformed other AI-based radiographic segmentation models, such as those developed by [PERSON] et al. for measuring acetabular component angles in total hip arthroplasty in humans. Their system, which focused on surgical outcomes and implant positioning, reported a DS of 0.90 for acetabular component segmentation in their anteversion angle model, the highest among their tested models [37]. The superior segmentation accuracy of our model highlights its potential to enhance diagnostic precision and streamline workflows, not only in veterinary medicine but possibly in future applications for human medicine. In addition to its overall performance, the automated FHC/DAE system offers a substantial advantage in processing time.