## 4 Discussion
In the future, as experts upload accurately labeled real-human-eye data from different regions and ethnicities to the system (with user consent), we will establish a universal pretrained real-human-eye model, which will have better generalization performance than the model-eye pretrained model. All relevant studies currently adopt a transfer learning approach based on pretraining followed by fine-tuning; we will explore the use of advanced transfer learning methods, such as domain adaptation, to better complete refractive error screening tasks. From a technical implementation perspective, existing methods have not fully explored the potential of eccentric photorefraction image features. While [PERSON] et al. [15] utilized a 24-near-infrared-LED multi-axial illumination system to collect 1216 eye images for training a regression model, their approach required manual extraction of reflected light intensity features and image stitching, resulting in complex feature engineering and limited generalization. [PERSON] et al. [16] proposed REDNet, which predicts refractive error using six-direction eccentric light source images, but its fixed eccentricity imaging scheme restricts the sensitive measurement range. This study innovatively employs a multi-eccentricity-angle, multi-channel spatial information dataset that better aligns with clinical refractive measurement principles. Notably, existing models generally suffer from the \"black-box\" problem. To address this, this study developed the Contrast Feature Guidance Module (CFG module), which integrates ophthalmic prior knowledge and enhances interpretability by leveraging feature space comparisons, ensuring transparency in clinical diagnostics. Due to differences in datasets and research objectives, direct horizontal comparisons are limited. However, this study achieved a breakthrough mean absolute error (MAE) of 0.168 D on the largest-scale dataset, surpassing REDNet's 0.174 D [16] and SURE-CNN's 0.653 D [14]. By replicating the REDNet six-image input architecture and conducting comparative experiments on Dataset1 and Dataset3, CFGN outperformed REDNet in key metrics: ACC1 (92.5, 92.0) vs. REDNet's ACC1 (88.9, 88.6) and MAE (0.230 D, 0.123 D) vs. REDNet's MAE (0.250 D, 0.198 D). At the same time, we compared the vision screening scheme based on eccentric photorefraction used in this study with schemes that utilize fundus images for vision screening. Our MAE was also lower than [PERSON]'s 0.56 D and [PERSON]'s [9] 0.63 D.  
Experiments conducted in two different regions validated the feasibility of this online refractive error screening solution, which is centered on the CFGN network and supplemented by a pretrained model based on model eyes. This innovative approach provides a new technical pathway for routine vision screening in various regions. The solution not only significantly improves screening efficiency but also greatly reduces screening costs, making it particularly suitable for areas with limited medical resources and professional technical personnel. By conducting routine refractive error screening, continuous refractive error data can be obtained, enabling the ongoing monitoring of vision changes and laying a solid data foundation for establishing predictive models for refractive error changes. As shown in Figure 18, the vision screening terminal based on eccentric photorefraction principles can be easily deployed in schools, communities, and other public places, providing self-service screening for adolescents and children. This system has been preliminarily validated in regions such as Jinzhou, Tacheng, and Changzhi. In summary, AI-based screening technology has established a comprehensive vision data monitoring system, providing a scientific basis for vision health management. This, in turn, assists schools, healthcare centers, and other institutions in building a collaborative vision prevention and control network, realizing a closed-loop management system for screening, monitoring, and intervention.