［Abstract］　The visual system of minors is not fully developed, and the visual impairment occurring at this critical stage is likely to destroy the normal visual development law and causes irreversible damage to the vision of the minors. Early screening and treatment is the key to reduce the visual damage of minors. Accelerating the research and development of medical artificial intelligence algorithm is expected to provide new ideas for large-scale population intelligent screening of visual impairment of minors and early prevention and treatment of visual impairment of minors.]]> 2020/3/16 15:42:27 LIN Hao-tian, LIN Duo-ru 5true ［Abstract］　In recent years, the increasingly developed technology of deep learning of artificial intelligence makes many fields gradually realize automatic intelligent work. In the field of medicine, with the developments of medical data electronization and internet medicine, it has become an inevitable trend to develop a new medical mode to realize computer-aided diagnosis systems based on convolutional neural networks, which includes positioning, segmentation and classification. Medical image segmentation technology is the difficulty and key point in the automatic analysis of medical image. At present, there are still many problems to be solved. In this paper, the progress of medical image segmentation will be systematically reviewed from three aspects: the characteristics of clinical medical image, the introduction of deep learning mainstream segmentation networks and the application of current medical image segmentation networks in clinical application, and the current development situation, challenges and future development direction of convolution neural networks in medical image segmentation task will also be analyzed.]]> 2020/3/16 15:42:27 YOU Qi-jing, WAN Cheng 4true ［Abstract］　Objective　To develop and evaluate an intelligent assistant diagnosis system based on deep learning algorithm for automatic recognition of corneal in vivo confocal microscopy(IVCM) images. Methods　IVCM images of the infectious keratitis patients in the Department of Ophthalmology, the People′s Hospital of Guangxi Zhuang Autonomous Region were included. ResNet101 convolution neural network was used to build the intelligent model and the effectiveness of the model was tested using a 5-fold cross-validation method. The accuracy, specificity and sensitivity of the model were calculated and were used to evaluate the ability of the intelligent assistant diagnosis system to identify fungal hyphae, inflammatory cells and activated dendritic cells. Results　A total of 2 105 images were included. The cross validation showed that the accuracy, specificity and sensitivity of identification of fungal hyphae were 0.974, 0.976 and 0.971, respectively, and those of identification of inflammatory cells were 0.993, 0.994 and 0.990, respectively, and those of identification of activated dendritic cells were 0.993, 0.994 and 0.990, respectively. Conclusion　The intelligent system based on deep learning algorithm developed in this study can effectively automatically recognize the abnormal keratitis cells in the confocal images, and has a good diagnostic performance in identifying the abnormal keratitis cells in a variety of IVCM images.]]> 2020/3/16 15:42:27 LYU Jian, CHEN Qi, ZHANG Kai, et al. 3true ［Abstract］　Objective　Deep convolutional neural networks was used to learn the chromosome images in the metaphase of mitosis and test its accuracy in chromosome classification. Methods　A total of 1 275 chromosome images of different individual metaphase divisions were included in this study, of which 735 cases were used as training sets for deep convolutional neural network, and 245 cases were used as test sets for internal validation, and 245 cases of another hospital′s data were used as external validation. In addition, 50 chromosome images were taken to record the time and accuracy of completing chromosome classification by human and computer. Results　The results of 24 categories indicated that the accuracy rate of the internal validation was 91.22% and that the accuracy rate of the external validation was 91.48%. The efficiency of classification by ResNet was more than 1 000 times higher than that by manual operation, and its accuracy was significantly better than that of non-genetic specialists. Conclusion　Deep convolutional neural networks has great potential in the application of chromosome classification and will be helpful to construct a platform for automatic chromosome karyotype analysis.]]> 2020/3/16 15:42:27 ZHANG Cheng-cheng, SONG Jie-ping, XU Shu-qin, et al. 2true ［Abstract］　Visual impairment is a serious threat to human health and quality of life. New intelligent diagnosis and treatment mode is urgently needed to meet the huge demand of blindness prevention and treatment. The development of medical artificial intelligence in ophthalmology has obvious disciplinary advantages, including that the eye is the observation window of multi organ health status, and ophthalmic diseases have high clinical safety and application scalability. The research mode of artificial intelligence in ophthalmology can provide important reference for clinical research and application exploration of artificial intelligence in other fields.]]> 2020/3/16 15:42:27 LIN Duo-ru, WU Xiao-hang, LIU Zhen-zhen 1true