We have made a statistic analysis on all the theses carried in the 31 issues of the journal of The Ocular Fundus published in the nine years from its first issue to the change of its title by using the method of informetrics.The results show that the journal carries 18.03 original papers each issue on the average,and the average citation is 6.54 articles each paper.The main sources of the information for the authors of the ocular fundus diseases in China is from the periodicals (78.65%).The language for the citation is mainly English(58.39%).The half-life periods for Chinese and English periodicals are 3.67 years and 8.42 years respectively.We recommend 4 Chinese periodicals,6 English periodicals and 1 Japanese periodical as the key periodicals of this special field.This study reflects the present situation and future trend of the special field of oculare fundus diseases of China's ophthalmology.It provides reference materials for authorship in this field and data for infomation science.It is also part of the summary about the journal of The Ocular Fundus. （Chin J Ocul Fundus Dis,1993,9:225-228）
Objective To automatically segment diabetic retinal exudation features from deep learning color fundus images. Methods An applied study. The method of this study is based on the U-shaped network model of the Indian Diabetic Retinopathy Image Dataset (IDRID) dataset, introduces deep residual convolution into the encoding and decoding stages, which can effectively extract seepage depth features, solve overfitting and feature interference problems, and improve the model's feature expression ability and lightweight performance. In addition, by introducing an improved context extraction module, the model can capture a wider range of feature information, enhance the perception ability of retinal lesions, and perform excellently in capturing small details and blurred edges. Finally, the introduction of convolutional triple attention mechanism allows the model to automatically learn feature weights, focus on important features, and extract useful information from multiple scales. Accuracy, recall, Dice coefficient, accuracy and sensitivity were used to evaluate the ability of the model to detect and segment the automatic retinal exudation features of diabetic patients in color fundus images. Results After applying this method, the accuracy, recall, dice coefficient, accuracy and sensitivity of the improved model on the IDRID dataset reached 81.56%, 99.54%, 69.32%, 65.36% and 78.33%, respectively. Compared with the original model, the accuracy and Dice index of the improved model are increased by 2.35% , 3.35% respectively. Conclusion The segmentation method based on U-shaped network can automatically detect and segment the retinal exudation features of fundus images of diabetic patients, which is of great significance for assisting doctors to diagnose diseases more accurately.
Single cell RNA sequencing technique provides a strong technical support for the analysis of cell heterogeneity in biological tissues, and has been widely used in biomedical research. In recent years, considerable scRNA-seq data have been accumulated in the research of ocular fundus diseases. The ocular fundus is abundant for the network of vessel and neuron, which leads to the complicated pathogenesis of fundus diseases. Through single cell RNA sequencing technique, the expression of thousands of genes of certain cell types or even subtypes can be obtained in the disease environment. Single cell RNA sequencing technique accurately reveals the pathogenic cell types and pathogenic mechanisms of ocular fundus diseases such as neovascular retinopathy, which provides a theoretical basis for the birth of new diagnosis and treatment targets. The construction of multi-omics single-cell database of ocular fundus diseases will enable high-quality data to be further explored and provide an analysis platform for ophthalmic researchers.