## 4 Discussion
On a workstation equipped with an NVIDIA RTX 3090 GPU with 24 GB of VRAM, the average processing time per radiograph was 264.8 milliseconds for segmentation and 99.2 milliseconds for keypoint detection, allowing the system to generate FHC/DAE measurements for both hip joints in under 0.5 s. Al though performance may vary with different hardware configurations, the system remains significantly faster than manual assessment. In comparison, an experienced examiner typically requires between 1 and 1.5 min per radiograph using Dys4 Vet's manual annotation tools, depending on image quality and complexity. By dramatically reducing annotation time, the automated system improves workflow efficiency and offers clear practical benefits for clinical use, particularly in large-scale screening scenarios or research environments. To validate the FHC/DAE system, its classifications and measurements were compared with expert examiners and their agreement with each other. Agreement between E1 and E2 was almost perfect (\(\kappa\) = 0.90 (95% CI [0.84, 0.95], \(p\) < 0.001). The contingency table shows that 80.7% of classifications fell within in the \"Superimposed\" (67 cases) and \"Lateral\" (46 cases) categories. Although this distribution could suggest that the high observed agreement (\(P_{0}\)) of 90% is influenced by prevalence, the weighted kappa value and narrow confidence interval confirm that agreement extends beyond chance. Importantly, disagreements were minimal (14 cases) and primarily confined to adjacent categories such as \"Superimposed\" versus \"Lateral\", which are less penalized in the linear weighting scheme. Clinically significant categories such as \"Lateral+\" (10 out of 11 agreements) and \"Luxation\" (1 out of 1 agreement) also demonstrated strong concordance. For continuous FHC/DAE measurements, inter-rater reliability between E1 and E2 was excellent (ICC = 0.97, 95% CI [0.96, 0.98], \(p\) < 0.001), with a SEM of 0.91 mm, indicating that approximately 68% of E2's measurements would fall within \(\pm\)0.91 mm of E1's, reinforcing the precision and consistency of the measurements. Agreement between E1 and the system was almost perfect (\(\kappa\) = 0.86 95% CI [0.80, 0.92], \(p\) < 0.001). The contingency table shows that 72.1% of classifications fell within the \"Superimposed\" (62 cases) and \"Lateral\" (39 cases) categories. Although this distribution reflects a common pattern in the dataset, it does not appear to overly inflate the kappa value, as demonstrated by the narrow confidence interval [28]. Importantly, disagreements were minimal (18 cases) and primarily confined to adjacent categories, limiting their clinical impact. Clinically significant categories such as \"Lateral+\" (9 out of 11 agreements) and \"Luxation\" (1 out of 1 agreement) also demonstrated strong concordance, supporting the system's ability to replicate E1's classifications across both frequent and less common categories, thus reinforcing its clinical applicability. For continuous FHC/DAE measurements, inter-rater reliability between E1 and the system was excellent (ICC = 0.97, 95% CI [0.95, 0.98], \(p\) < 0.001), with an SEM of 0.92 mm, indicating that approximately 68% of the system's measurements would fall within \(\pm\)0.92 mm of E1's. Given that categories such as \"Lateral\" (\(-\)5 to \(<\)\(-\)1 mm) and \"Lateral+\" (\(-\)10 to \(-\)6 mm) each have a 4 mm range, this small error margin remains well within acceptable limits, reducing the likelihood of clinically significant misclassifications and supporting the system's ability to replicate expert assessments with minimal variability. The comparison between E2 and the system showed substantial agreement (\(\kappa\) = 0.79, 95% CI [0.72, 0.86], \(p\) < 0.001), with an observed agreement (\(Po\)) of 80%. Although this was slightly lower than the agreement observed with E1, it aligns with the study design, as E1 provided the ground truths used to train the system.