With the emergence of unmanned plane, autonomous vehicles, face recognition, and language processing, the artificial intelligence (AI) has remarkably revolutionized our lifestyle. Recent studies indicate that AI has astounding potential to perform much better than human beings in some tasks, especially in the image recognition field. As the amount of image data in imaging center of ophthalmology is increasing dramatically, analyzing and processing these data is in urgent need. AI has been tried to apply to decipher medical data and has made extraordinary progress in intelligent diagnosis. In this paper, we presented the basic workflow for building an AI model and systematically reviewed applications of AI in the diagnosis of eye diseases. Future work should focus on setting up systematic AI platforms to diagnose general eye diseases based on multimodal data in the real world.

PurposeTo develop a new intelligent system based on deep learning for automatically optical coherence tomography (OCT) images categorization.MethodsA total of 60,407 OCT images were labeled by 17 licensed retinal experts and 25,134 images were included. One hundred one-layer convolutional neural networks (ResNet) were trained for the categorization. We applied 10-fold cross-validation method to train and optimize our algorithms. The area under the receiver operating characteristic curve (AUC), accuracy and kappa value were calculated to evaluate the performance of the intelligent system in categorizing OCT images. We also compared the performance of the system with results obtained by two experts.ResultsThe intelligent system achieved an AUC of 0.984 with an accuracy of 0.959 in detecting macular hole, cystoid macular edema, epiretinal membrane, and serous macular detachment. Specifically, the accuracies in discriminating normal images, cystoid macular edema, serous macular detachment, epiretinal membrane, and macular hole were 0.973, 0.848, 0.947, 0.957, and 0.978, respectively. The system had a kappa value of 0.929, while the two physicians' kappa values were 0.882 and 0.889 independently.ConclusionsThis deep learning-based system is able to automatically detect and differentiate various OCT images with excellent accuracy. Moreover, the performance of the system is at a level comparable to or better than that of human experts. This study is a promising step in revolutionizing current disease diagnostic pattern and has the potential to generate a significant clinical impact.Translational RelevanceThis intelligent system has great value in increasing retinal diseases' diagnostic efficiency in clinical circumstances.

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Fireworks-related ocular injuries occur mainly in children, males and rural settings, are frequently severe and visually devastating. Therefore, preventive measures should be strengthened, including public education and legal restriction on the sale and use of fireworks.

Aims: Herpes simplex virus type-1-induced herpes simplex keratitis (HSK) is a common immunological cornea disease. While previous studies have addressed the role of tumor necrosis factor (TNF)-α and matrix metalloproteinases (MMPs) in HSK, the mechanistic link between TNF-α and MMPs in the pathogenesis of HSK remains elusive. Methods: We first established a HSK mice model and measured the levels of TNF-α, MMP-2 and MMP-9 in the corneas at different time points by ELISA. Next, we employed cultured human corneal epithelial (HCE) cells as an in vitro model and performed gelatin zymography analysis. Results: We observed that the change in the TNF-α level shared a similar pattern to that of MMP-2 and MMP-9 in the HSK mice model. Furthermore, TNF-α stimulated MMP-2 and MMP-9 activities in a dose-dependent manner, but either knockdown of focal adhesion kinase (FAK) by short interference RNA or inhibition of extracellular regulated protein kinase (ERK) by chemical inhibitor could block TNF-α-stimulated MMP-2 and MMP-9 activities in vitro. Taken together, our results provide in vivo evidence that the TNF-α level is positively correlated with MMP-2 and MMP-9 levels in a HSK model and in vitro evidence that TNF-α stimulates MMP-2 and MMP-9 activities via the activation of FAK/ERK signaling in HCE cells. Conclusions: Our findings shed new light on the pathogenesis of HSK and open up new possibility of modulating the TNF-α-FAK-ERK signaling cascade to pursue therapeutic measures for HSK.

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