## 5 Discussion
Although comparisons between models reveal differences in AUC, these differences are not statistically significant. The confidence intervals for the AUC differences include zero, indicating insufficient evidence to claim a significant difference in discriminative ability between the models. Consequently, the performance of the models in terms of AUC appears comparable, and observed differences are likely due to random variability rather than true variations in predictive power. This suggests that while slight variations in AUC exist, none are large or consistent enough to conclusively determine that one model outperforms the others. Furthermore, since myopic patients often report symptoms related to poor distance vision, diagnosing myopia can be aided by patient interviews about vision quality. In this context, lower specificity can be acceptable if the model maintains high recall. The primary risk in a screening program is failing to identify individuals with the disease, so high recall ensures that most myopic individuals are correctly identified, reducing false negatives. While lower specificity means more healthy individuals may be incorrectly labeled as positive, this is less critical in screening as false positives can generally be managed through follow-up diagnostic tests. By using the proposed model, eye specialists and volunteers can significantly enhance the precision of the screening system. Their involvement, even in simple tasks such as asking the right questions, can help further reduce the chair time required for a comprehensive eye exam, which typically includes both aided and unaided visual acuity testing, as well as objective and subjective refraction. This study, while valuable, has several limitations that need to be addressed in future research and clinical implementation. A major limitation is the relatively small and imbalanced dataset of myopic and non-myopic images, which limits the model's ability to generalize to a broader population. For the model to be more effective in clinical practice, it is crucial to have a larger, more balanced dataset that includes an equal representation of both myopic and non-myopic cases. Currently, the dataset is skewed towards myopic patients, which may affect the model's performance and accuracy. To overcome this, it is essential to ensure that both classes are balanced during training, which would help improve the model's ability to make accurate predictions across different population groups. Additionally, for future improvements, it may be beneficial to focus on specific age groups, or multiple age groups, to test the model's performance within defined age ranges. Age-related variations in myopia could influence the model's ability to classify accurately, and this could be addressed by testing the model within selected age categories. Similarly, establishing a specific SER range for myopic patients could improve the model's performance. Including more images from patients with low myopic SER (close to \(-0.50\) D) and higher hypermetropic SER would help refine the model, particularly in distinguishing between low myopia and hyperopia, which are conditions the model may currently struggle with, potentially leading to misclassification. Lastly, the use of transfer learning could be explored as a means to improve the model's performance further. Transfer learning would allow the model to leverage pre-trained networks on larger, more diverse datasets, enhancing its generalization ability and potentially improving accuracy across varied populations. In terms of future developments, an important step would be to repeat the experiment and compare the results, using more recent versions of the YOLO algorithm beyond YOLOv8, which was used in this study. Updated versions of YOLO, such as YOLOv10, YOLOv11, or future iterations, come with improvements in accuracy, efficiency, and generalization ability.