Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce the rate of blindness in cataract patients. However, the professional knowledge of ophthalmologists is necessary for the clinical cataract detection. Therefore, the potential costs may make it difficult for the widespread use of cataract detection to prevent blindness. Artificial intelligence assisted diagnosis based on medical images has attracted more and more attention of researchers. Many studies have focused on the use of pre-defined feature sets for cataract classification, but the predefined feature sets can be incomplete or redundant. On account of the above issues, some studies have proposed deep learning methods to automatically extract image features, but all based on global features and none has analyzed the layer-by-layer transformation process of the middle-tier features. This paper uses Convolutional Neural Networks (CNN) to learn useful features directly from input data, and Deconvolution Network (DN) method is employed to investigate how CNN characterizes cataract layer-by-layer. We found that compared to the global feature set, the detail vascular information which is lost after multi-layer convolution calculation also plays an important role in cataract grading task. And this finding fits with the morphological definition of fundus image. Through the finding, we gained insights into the design of hybrid Global-local feature representation model to improve the recognition performance of automatic cataract grading.

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is considered the most common cause of blindness. Early diagnosis and treatment can reduce the suffering of patients and prevent visual impairment from turning into blindness. Recently, cataract diagnosis applying pattern recognition is in a rising period. For retinal fundus images, the task is usually cataract classification. However, it needs complex manual processing, which demands dexterous people and time taking exertion. Besides, it faces the challenge of effective interpretability and dependability. In this paper, we develop a deep-learning algorithm to intuitively identify cataract attributes to solve these limitations. Our model, is a 18(50)-layer convolutional neural network that inputs retinal image in G channel and outputs the prediction with heatmap. The heatmap localizes the areas where most indicative of different levels of cataract. Furthermore, we extend the training strategy for the corresponding task, which aims at improving the performance of the network. Comparing with other methods in cataract classification, we succeeded to achieve state of the art accuracy of proposed method on detection and grading task. Most importantly, our model provides a compelling reason via localizing the areas revealing cataract in the image.

Retinal diseases, such as glaucoma, age-related macular degeneration, and high myopia, are major contributors to global vision loss, emphasizing the need for early detection and intervention. Current deep learning approaches for diagnosing retinal diseases using fundus images primarily focus on single-disease classification due to the scarcity and expense of diverse datasets. This limitation restricts their generalization across multiple ocular diseases and impedes transfer learning to untrained disease types. In this paper, we introduce a novel Model-Agnostic Meta-Learning framework, called Mirror Meta-Learning (MML) , which incorporates an autoencoder module to supervise the backpropagation path in few-shot learning, enhancing model initialization and adaptation. MML’s effectiveness is validated using four publicly available retinal disease binary classification datasets and a proprietary high myopia dataset. In addition, MML demonstrates robustness when tested on three well-established few-shot learning datasets. Our results show the proposed model’s superiority in terms of performance and generalizability in ocular disease classification tasks.

Background: Both telomere length and alcohol consumption play important roles in carcinogenesis and biological age. Many efforts have been made to investigate the association between alcohol consumption and telomere length. However, no consensus has been reached yet. Methods: In this article, we performed a meta-analysis to integrate the investigation results in the literature about the association between alcohol consumption and telomere length. After searching articles published between 2000 and 2016, 21 articles (including 27 analyses, total sample size 35,891) met our eligibility criteria. Results: We found a significant association between alcohol consumption and telomere length (Fisher’s combined p-value = 3.52E-8 and Liptak’s weighted p-value = 8.24E-3). We also found that the significance of the association between alcohol consumption and telomere length varies with study type (cohort, case-control, or cross-sectional) and study population (Europe, Asia, American, or Australia). Conclusions: Combined evidence showed that alcohol consumption is associated with telomere length. The consistent quantifications of alcohol consumption and telomere length would benefit the future aggregation of the evidence from different studies.