## 4 Discussion  
Nowadays, the issue of early-onset myopia has become increasingly severe, especially with the widespread adoption of the internet and electronic devices. The age at which children and adolescents first interact with electronic devices continues to decline. Moreover, the prevalence of paperless learning and electronic whiteboards in education has significantly increased their screen time on smartphones and tablets, leading to a continuous rise in myopia incidence among younger populations. However, the preventive screening of vision health in adolescents remains difficult to implement on a regular basis due to high labor costs and a lack of professional equipment. Traditional refractive measurement techniques can no longer meet the demands of modern society. To address this, this study proposes an innovative solution that integrates eccentric photorefraction and deep learning, aiming to establish an efficient, accurate, and cost-effective online vision screening system. Previous studies have faced limitations due to insufficient data scale and imbalanced distribution. [PERSON] et al. (2018) utilized only a dataset of 305 images, with only 12 samples having \(\geq\)+5.0 D and all from a single source; [PERSON] et al. (2018) expanded their dataset across two regions but were still limited to 512 images. This study collected data from two regions 3900 km apart, each with over 6000 samples, ensuring adequate model convergence and validating the generalizability of the proposed solution. In addition, model-eye data were used to alleviate the lack of high-diopter hyperopia data. Existing studies primarily focus on refractive status classification: [PERSON] et al. (2018) defined a seven-level classification standard, [PERSON] et al. (2018) developed SURE-CNN using \(-2.5\) D as a threshold for binary classification alerts, and [PERSON] et al. (2018) set \(-2\) D as the classification boundary. While such \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset1** & \
\begin{tabular}{c} **CFG** \\ **Module** \\ \end{tabular} &
\begin{tabular}{c} **Bottom** \\ **Transformer** \\ \end{tabular} & \(ACC0.5\) (\(\%\)) & \(ACC1\) (\(\%\)) & \(MAE\) \\ \hline \multirow{4}{*}{baseline} & \(\times\) & \(\times\) & 67.4 & 90.1 & 0.269 \\  & \(\times\) & \(\times\) & 68.8 & 92.2 & 0.236 \\ \cline{1-1}  & \(\times\) & \(\checkmark\) & 68.8 & 92.0 & 0.235 \\ \cline{1-1}  & \(\checkmark\) & \(\checkmark\) & 69.6 \(\uparrow\) & 92.5 \(\uparrow\) & 0.230 \(\uparrow\) \\ \hline \hline \end{tabular} \(\dagger\) represents the optimal experimental result.  
\end{table} 
Table 7: Result of model optimization. methods can identify myopia risks, they suffer from rigid thresholds that limit screening sensitivity and prevent precise refractive measurement. This study innovatively replaces classification models with a regression model and introduces an error range evaluation system, significantly improving the clinical applicability of screening results. In terms of model training strategies, previous studies commonly employed transfer learning based on ImageNet pretrained parameters. However, natural images differ significantly from eccentric photorefraction images in feature characteristics. This study constructed a model-eye system, leveraging the similar optical properties of real human eyes and model eyes under eccentric infrared illumination to generate highly clinically relevant simulated data, thereby enhancing data diversity. Transfer experiments demonstrated that the pretrained model based on model eyes outperformed the ImageNet pretrained model, requiring less training data while achieving greater performance improvements. Although generative adversarial networks (GANs) could serve as an alternative for data synthesis, their application in medical image generation relies on large-scale training data, making them unsuitable for this study's specific scenario.