###### Abstract  
With the development of the internet, the incidence of myopia is showing a trend towards younger ages, making routine vision screening increasingly essential. This paper designs an online refractive error screening solution centered on the CFGN (Comparative Feature-Guided Network), a refractive error screening network based on the eccentric photorefraction method. Additionally, a training strategy incorporating an objective model-eye pretraining model is introduced to enhance screening accuracy. Specifically, we obtain six-channel infrared eccentric photorefraction pupil images to enrich image information and design a comparative feature-guided module and a multi-channel information fusion module based on the characteristics of each channel image to enhance network performance. Experimental results show that CFGN achieves an accuracy exceeding 92% within a \(\pm\)1.00 D refractive error range across datasets from two regions, with mean absolute errors (MAEs) of 0.168 D and 0.108 D, outperforming traditional models and meeting vision screening requirements. The pretrained model helps achieve better performance with small samples. The vision screening scheme proposed in this study is more efficient and accurate than existing networks, and the cost-effectiveness of the pretrained model with transfer learning provides a technical foundation for subsequent rapid online screening and routine tracking via networking. refactive error screening; pretrained model; deep learning; transfer learning; objective model eye +
Footnote †: journal:  
0000-0001-6000-0000-00001-6000][PERSON]  
0000-0001-6000-0000-0000-00001-6000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000  
error screening can help detect issues like myopia, hyperopia, and astigmatism early in adolescence, allowing timely intervention to slow vision decline. However, traditional refractive error screening techniques have issues such as complex instrument operation, high labor costs, and low cooperation from children. Additionally, in underdeveloped regions, especially in many township health centers, the lack of basic medical equipment and trained professionals makes it difficult to conduct regular vision screening. Within the realm of adolescent health protection, there is a pressing demand for more efficient and effective screening and diagnostic tools [3]. Artificial intelligence (AI) technology has been successfully applied in the healthcare field [4; 5]. [PERSON] et al. [6] presented a novel approach that combines deep learning and fair AI technologies for screening autism spectrum disorder (ASD) in both children and adults. This method significantly improves diagnostic accuracy and emphasizes the importance of reducing AI biases. [PERSON] et al. [6] demonstrated the potential of TinyML for real-time detection of respiratory diseases, such as asthma, on low-power devices. This offers a portable diagnostic tool for remote and resource-limited areas. Researchers have also proposed deep learning-based methods for refractive error measurement. [PERSON] et al. [7] first proposed using deep learning for refractive error detection in 2018. They employed a hybrid structure combining ResNet [8] and soft attention for regression tasks, achieving an MAE of 0.56 D. [PERSON] et al. [9] utilized a deep learning system based on a fusion model (FMDLS) to train on 11,973 retinal fundus images, successfully identifying spherical, cylindrical, and axial refractive errors, demonstrating good consistency with cycloplegic refraction. This suggests that deep learning can be applied to make novel predictions based on medical imaging. Exploring how to use AI techniques to efficiently and cost-effectively complete refractive error screening tasks holds great research potential. However, the lack of accurately labeled medical imaging data has hindered the development of deep learning-based refractive error detection methods.