## 1 Introduction
reported a poor-to-fair level of agreement, with an intraclass correlation coefficient (ICC) of 0.47 and a Cohen's kappa value of 0.29, indicating substantial inconsistency among examiners [12]. The inherent variability observed in these findings, likely stemming from the high level of technicalprofficiency required for precise NA measurement, including ambiguities in defining the effective cranialateral acetabular rim (CrAR) [12; 13], highlights why the NA should not serve as the primary or most heavily weighted metric within CHD scoring protocols. The FHC/DAE distance directly evaluates the horizontal positioning of the femoral head relative to the edge of the acetabulum, indicating whether the femoral head is sufficiently covered by the acetabulum or if it is positioned outward [4], which would signal joint laxity and potential instability. Even though it seems to share functional overlap with the NA, this redundancy can help address the limitations of the NA, especially in cases where it may not detect dogs at risk for DJD. While it may not have the same widespread recognition as the NA, [PERSON] et al. showed that excluding FHC/DAE from the Fluckiger system resulted in a 21.24% increase in false negatives, indicating that more cases of CHD were under-diagnosed when FHC/DAE was omitted. By contrast, omitting NA increased false negatives by only 11.06%, underscoring the critical role of FHC/DAE in capturing lateral femoral head displacement, which NA alone might overlook. Furthermore, the study found that concordance with the original Fluckiger system was higher when NA was omitted (84.96%) compared to FHC/DAE (78.32%) [7]. This suggests that FHC/DAE provides unique insights into hip joint conformation, adding specificity to CHD diagnosis that NA cannot fully replace. Advances in technology have enabled the development of computer-aided detection (CAD) systems for medical imaging, significantly enhancing diagnostic precision [14]. While early CAD systems often struggled with low precision, deep learning (DL) algorithms have demonstrated remarkable improvements, achieving near-human performance in many imaging tasks [14]. This progress has spurred interest in using DL techniques to address diagnostic challenges in veterinary medicine. [PERSON] et al. initially utilized a partial least squares discriminant analysis model and a nonlinear neural network to classify VDHE radiographs as either \"hip\" or \"not hip\", resulting in classification errors of 6.7% and 8.9%, respectively [15]. Building upon this foundation, they later developed a two-step method using a YoloV3 Tiny, a convolutional neural network (CNN), to detect and isolate regions of interest for each hip joint, achieving an intersection over union (IoU) score of 0.85, indicating strong agreement with ground truth data. In the second step, another YoloV3 Tiny CNN was trained for binary classification of CHD (i.e., distinguishing healthy from dysplastic hips). Despite reducing computational complexity by focusing on the regions of interest, this approach achieved a sensitivity of only 53% for identifying dysplastic cases. Subsequently, they applied a YoloV3 Tiny CNN to detect and categorize hip joints using FCI criteria, achieving a sensitivity of 53% and a specificity of 92% [16]. Similarly, [PERSON] et al. applied a pre-trained Inception-V3 model for binary classification, attaining 83% sensitivity for dysplastic cases but encountering specificity challenges, with a rate of 66% due to false positives [17]. [PERSON] et al. used a DL model known as EfficientNet to classify CHD in radiographic images, achieving an area under the receiver operating characteristic curve (AUC) of 0.964 and 89.1% accuracy in binary classification, and an AUC of 0.913 for FCI grading [18]. Conversely, [PERSON] et al. explored a different approach, utilizing a 3D CNN trained on magnetic resonance imaging scans, attaining 89.7% accuracy in binary classification and highlighting the potential of volumetric analysis for comprehensive CHD assessment [19].