No abstract

Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets. These datasets are fed into the CNN to extract high-level features and implement automatic classification and grading. To demonstrate the performance and effectiveness of the deep features extracted in the CNN, we investigate the features combined with support vector machine (SVM) and softmax classifier and compare these with the traditional representative methods. The qualitative and quantitative experimental results demonstrate that our proposed method offers exceptional mean accuracy, sensitivity and specificity: classification (97.07%, 97.28%, and 96.83%) and a three-degree grading area (89.02%, 86.63%, and 90.75%), density (92.68%, 91.05%, and 93.94%) and location (89.28%, 82.70%, and 93.08%). Finally, we developed and deployed a potential automatic diagnostic software for ophthalmologists and patients in clinical applications to implement the validated model.

There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.

Sepsis-induced myocardial dysfunction is a major contributor to the poor outcomes of septic shock. As an add-on with conventional sepsis management for over 15 years, the effect of Xuebijing injection (XBJ) on the sepsis-induced myocardial dysfunction was not well understood. The material basis of Xuebijing injection (XBJ) in managing infections and infection-related complications remains to be defined. A murine cecal ligation and puncture (CLP) model and cardiomyocytes in vitro culture were adopted to study the influence of XBJ on infection-induced cardiac dysfunction. XBJ significantly improved the survival of septic-mice and rescued cardiac dysfunction in vivo. RNA-seq revealed XBJ attenuated the expression of proinflammatory cytokines and related signalings in the heart which was further confirmed on the mRNA and protein levels. Xuebijing also protected cardiomyocytes from LPS-induced mitochondrial calcium ion overload and reduced the LPS-induced ROS production in cardiomyocytes. The therapeutic effect of XBJ was mediated by the combination of paeoniflorin and hydroxysafflor yellow A (HSYA) (C0127-2). C0127-2 improved the survival of septic mice, protected their cardiac function and cardiomyocytes while balancing gene expression in cytokine-storm-related signalings, such as TNF-α and NF-κB. In summary, Paeoniflorin and HSYA are key active compounds in XBJ for managing sepsis, protecting cardiac function, and controlling inflammation in the cardiac tissue partially by limiting the production of IL-6, IL-1β, and CXCL2.