## 5 Discussion  
We created an image dataset consisting of fundus pictures of myopic and healthy eyes, categorized by eye SER. This dataset holds great potential for future research aimed at developing AI-based methods for objective myopia detection. The possibility of expanding it to include other eye diseases detectable through fundus images further strengthens its value, enhancing the applicability of computer vision techniques in clinical settings and offering new opportunities to improve patient care. We explored the feasibility of using CNN-based software with YOLOv8 architecture and its variants to diagnose myopia from single-eye fundus images, requiring no coding expertise. The model we developed, based on the large version of YOLOv8, represents a novel approach to myopia detection. It is the first model trained on a Caucasian population from Northern Italy and the first utilizing this state-of-the-art convolutional neural network. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **ACC** & **P** & **R** & **Sp** & **F1 Score** \\ \hline
**YOLOv8-large** & **0.80** & **0.838** & **0.886** & **0.600** & **0.861** \\ \hline YOLOv8-large (previous experiment) & 0.820 & 0.842 & 0.914 & 0.600 & 0.877 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results obtained by the proposed approach with the large model of YOLOv8 on the testing set. The input image size is 1024 \(\times\) 1024 and the batch size is 8. Figure 10: Confusion matrix from large model of YOLOv8 tested on testing set, with number of TP, TN, FP, and FN. Input image size is 1024 \(\times\) 1024 and batch size is 8. Retinal fundus imaging, a widely implementable technique, proves to be an effective tool for large-scale screening, especially in areas with limited access to eye specialists. The time required for retinography is minimal--around 1.5 min per patient--and it is an efficient alternative to other clinical tests like objective and subjective refraction. In our study, retinography was performed on both eyes consecutively for each patient. The combination of fundus imaging and machine learning highlights the promising potential of this approach for advancing clinical diagnostics. The performance metrics of the proposed network are promising, with balanced precision and recall across all experiments. This balance indicates a relatively low cost associated with false negatives, which occur when myopic images are incorrectly predicted as non-myopic. The model's high precision significantly reduces the risk of misclassifying myopic patients as non-myopic thereby preventing them from being excluded from the screening process. Additionally, the higher recall (or sensitivity) achieved by the larger YOLOv8 model across all experiments enhances the clinician's ability to detect myopia, reducing the risk of misdiagnosing a myopic eye. Consistently high precision, recall, F1 scores, and AUC values, all exceeding 0.8 (a clinically acceptable threshold for computer-assisted methods), demonstrate the model's potential to minimize misdiagnosis. Compared to other models presented in [32] with the same goal of classifying myopia from retinal fundus images, the model in this study achieves slightly lower accuracy across all YOLOv8 versions considered. However, we must consider differences in training parameters, such as batch size and input image size, as well as the various preprocessing and data augmentation methods applied. Furthermore, the two datasets differ, which may explain some of the variations in the results. Additionally, ref. [32] does not specify the number of images per class, so we cannot assess whether the dataset was well balanced or what stage of myopia was considered when training the models. Nonetheless, we are satisfied with presenting a series of functional models, trained on a well-defined dataset, despite some limitations. Our analysis of the performance metrics provides a clearer understanding of our algorithms' potential and the areas for further improvement.