## 4 Discussion
This integrated approach ensures a more thorough and reliable evaluation of CHD, reducing the potential impact of variability of any single metric. Looking ahead, the development of comprehensive AI-assisted diagnostic platforms that integrate multiple automated metrics may further enhance the objectivity, reproducibility, and clinical utility of CHD radiographic evaluation. The Dys4 Vet software, currently under development, is one such initiative that aims to consolidate interpretable metrics, including the FHC/DAE measurement, into a unified framework for decision support. In this context, the FHC/DAE system serves as a foundational component, contributing precise and standardized data to a broader diagnostic strategy. Importantly, these technologies are not intended to replace expert clinical judgment. While AI can assist by offering consistent and quantifiable assessments, it does not replicate the contextual understanding, ethical reasoning, or empathetic engagement that are central to veterinary practice. Sensitivity to animal behavior, nuanced clinical interpretation, and compassionate care remain uniquely human competencies. As such, platforms like the Dys4 Vet software should be viewed as tools to support and inform veterinary decision-making, not substitutes for professional expertise. A key strength of this study is the diversity of the dataset, which includes dogs from two distinct geographical regions, Portugal and Denmark. While the specific breeds of all dogs used in model development were not fully available due to metadata limitations, the inclusion of animals from different regions inherently introduces breed variation. Additionally, the test set, for which breed data were available, consisted of a diverse range of breeds, further supporting the system's ability to generalize. Furthermore, the use of different X-ray equipment introduced natural variations in image quality and anatomical presentation, enhancing the robustness of the automated system. This variability reduces the risk of overfitting to specific acquisition parameters and reinforces its applicability across different clinical settings and animal populations. However, since breed-specific biases could not be fully assessed for the training set, further studies with fully annotated breed metadata would be beneficial for a more comprehensive evaluation of potential breed-related effects. A key limitation of this study is that the system does not account for uncertainty in its classifications, leading it to provide definitive predictions even in cases where its confidence is low. This was particularly evident in the slight divergence observed between E2 and the system, which highlights the potential for variability in classifications, especially in borderline cases. While overall agreement remained strong, this discrepancy suggests that the model may struggle with ambiguous cases where even expert judgment varies, introducing potential inconsistencies in classification. These discrepancies appear to be more closely associated with technical image quality, such as poor contrast, blurred anatomical edges, or low image resolution. It is worth noting that the model was trained on resized, lower-resolution images, and the resulting predictions were subsequently upscaled to the original resolution. Although a smoothing filter was applied after prediction, this process may still lead to some inaccuracies. On the other hand, the FHC/DAE system demonstrated consistent performance across mild-to-severe CHD cases, suggesting that inaccuracies are not strongly influenced by disease-related anatomical variation. Without a mechanism to assess and indicate uncertainty, the system may misclassify difficult cases instead of flagging them for expert review. To address this, future improvements could incorporate uncertainty quantification methods, allowing the system to assess its confidence in each classification.