Prevalence of and risk factors for myopia among urban and rural children in Northeast China: protocol for a school-based cross-sectional study.

INTRODUCTION: The worldwide prevalence of myopia is high and continues to increase. In this study, a school screening programme for myopia will be implemented using the whole-process information method. The purpose of this study is to investigate the prevalence of myopia in urban and rural areas of Northeast China and to determine the factors related to myopia. METHODS AND ANALYSIS: This is a school-based cross-sectional study. Our study population will include 6000 school-aged children from 2 urban and 2 rural schools in Jinzhou, China. The study will be conducted using our self-developed internet-based intelligent data collection, transmission, storage and analysis system. Examination parameters include uncorrected distance visual acuity, presenting distance visual acuity, non-cycloplegic autorefraction, height, weight, waist circumference, hip circumference, spinal curvature and dental caries. The examination report will be automatically sent to parents, who will complete the questionnaire, and appropriate statistical analysis will be performed. The main outcome is the prevalence of myopia, defined as an equivalent spherical degree ≤-0.5 D. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Third Affiliated Hospital of Jinzhou Medical University (number: JYDSY-KXYJ-IEC-2023-018). Findings will be published in a peer-reviewed journal. Subjects and their parents (or other authorised agents) give informed consent prior to study participation. TRIAL REGISTRATION NUMBER: ChiCTR2300072893.

[Infrared Imaging Meibomian Gland Segmentation System Based on Deep Learning].

In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition. Thirdly, a large number of meibomian gland images are collected to train and verify the semantic segmentation network, and the clarity evaluation index is used to verify the meibomian gland enhancement effect. The experimental results show that the similarity coefficient of the proposed method is stable at 92.71%, and the image clarity index is better than the similar dry eye detection instruments on the market.

Deep learning for predicting refractive error from multiple photorefraction images.

BACKGROUND: Refractive error detection is a significant factor in preventing the development of myopia. To improve the efficiency and accuracy of refractive error detection, a refractive error detection network (REDNet) is proposed that combines the advantages of a convolutional neural network (CNN) and a recurrent neural network (RNN). It not only extracts the features of each image, but also fully utilizes the sequential relationship between images. In this article, we develop a system to predict the spherical power, cylindrical power, and spherical equivalent in multiple eccentric photorefraction images. Approach First, images of the pupil area are extracted from multiple eccentric photorefraction images; then, the features of each pupil image are extracted using the REDNet convolution layers. Finally, the features are fused by the recurrent layers in REDNet to predict the spherical power, cylindrical power, and spherical equivalent. RESULTS: The results show that the mean absolute error (MAE) values of the spherical power, cylindrical power, and spherical equivalent can reach 0.1740 D (diopters), 0.0702 D, and 0.1835 D, respectively. SIGNIFICANCE: This method demonstrates a much higher accuracy than those of current state-of-the-art deep-learning methods. Moreover, it is effective and practical.