## 4 Discussion
The horizontal split essentially divides the image into two halves that may not equally contribute to the accurate classification of the knee joint's condition, leading to a skewed or incomplete understanding of the disease's manifestation. Furthermore, the underperformance of the Region of Interest (ROI) model can be contrastively elucidated by examining why the 'No ROI' model performed exceptionally well for the extreme classes. It has been recognized that patient age correlates with the progression and stage of osteoarthritis (OA). Interestingly, neural networks might still infer age-related information even when the knee joint is absent from the image. Indicators such as faint signs of sclerosis and the general condition of the remaining knee radiograph may inadvertently provide age-related clues. This is particularly relevant in the later stages of OA, where osteophytes can be exceptionally large and located further from the knee joint. Consequently, we suspect that age may be a confounding variable that the models are inadvertently leveraging. Such a hypothesis warrants further investigation that would help to explain both the 'ROI' underperformance and the 'No ROI' overperformance at the extremes. To further investigate this hypothesis, future studies should consider directly targeting age as a classification (or regression) variable. Utilizing the ROI, Baseline, and Non-ROI model sets in this context could provide valuable insights into the extent to which age can be predicted from knee radiographs. Such an approach would not only help in understanding the potential confounding influence of age on OA automatic grading but also contribute to the broader discourse on the interpretability and reliability of neural network decisions in KOA medical imaging. This exploration into the intersection of age, disease markers, and AI model performance might be crucial for refining automatic tools and ensuring that they are as accurate and unbiased as possible. This setup, focusing on the relationship between age and OA manifestations in knee radiography and its impact on AI models, warrants further investigation and could potentially lead to more nuanced and accurate classification methods in the future. In this endeavor, we also consider the participation of medical experts mandatory for validation purposes, and broader discussions on the clinical implications of the outcomes. Our findings regarding pixel noise underscore an essential consideration in raw radiograph data, which may contain parts of suboptimal quality with substantial pixel noise. These observations align with similar findings reported in other studies [37; 38]. The classifier sensitivity to noise was particularly acute in the no-OA to early grade classes (KL0-1), where even a minimal noise level of 5 %, led to a decline in early grade performance. This decline in early grade performance might be explained by the point that noise may falsely trigger or mask faint osteeophyte features or other similar features in the images which are characteristic of early stages OA, leading to misinterpretations by the deep learning model during training. Moreover, a critical issue in digitized X-rays is the severe aliasing problems that may occur. Aliasing can introduce what is known as aliasing-associated noise. Such noise may interfere by creating misleading artifacts. Overall noise artifacts raise questions about the inclusion of low-quality radiographs in classification systems. They also underscore the necessity for research aimed at establishing guidelines for acceptable levels of noise, including aliasing-associated noise. One possible intervention could involve incorporating blur effects in the image augmentation process. However, it's important to note that the effectiveness of this method specifically addresses pixel noise, and its impact on aliasing issues remains unclear. On the other hand, the effect of aliasing and methods to mitigate it on the performance of these systems is an area that is not yet well understood and requires further investigation.