Identification of diabetic retinopathy lesions in fundus images by integrating CNN and vision mamba models

by Zenglei Liu, Ailian Gao, Hui Sheng, Xueling Wang

Diabetic retinopathy, a retinal disorder resulting from diabetes mellitus, is a prominent cause of visual degradation and loss among the global population. Therefore, the identification and classification of diabetic retinopathy are of utmost importance in the clinical diagnosis and therapy. Currently, these duties are extensively carried out by manual examination utilizing the human visual system. Nevertheless, manual examination is sometimes arduous, time-consuming, and prone to errors. Deep learning-based methods have recently demonstrated encouraging results in several areas, such as image categorization and natural language mining. The majority of deep learning techniques developed for medical image analysis rely on convolutional modules to extract the inherent structure of images within a certain local receptive field. Furthermore, transformer-based models have been utilized to tackle medical image processing problems by capitalizing on global connections among distant pixels in the images. Considering these analyses, this work presents a comprehensive deep learning model that combines convolutional neural network and vision mamba models. This model is designed to accurately identify and classify diabetic retinopathy lesions displayed in fundus images. Furthermore, the vision mamba component incorporates the bidirectional state space method and positional embedding to enable the location sensitivity of visual data samples and meet the conditions for global relationship context. An evaluation of the suggested method was carried out by comparison experiments between state-of-the-art algorithms and the proposed methodology. Empirical findings demonstrate that the suggested methodology surpasses the most advanced algorithms on the datasets that are accessible openly. Hence, the suggested approach may be regarded as a helpful tool for therapeutic processes.

Effectiveness of shared decision‐making for mode of delivery after caesarean section: A systematic review and meta‐analysis of randomized controlled trials

Abstract

Aim

To review the content, format and effectiveness of shared decision-making interventions for mode of delivery after caesarean section for pregnant women.

Design

Systematic review and meta-analysis.

Methods

Six databases (PubMed, Web of science Core Collection, Cochrance Network, Embase, CINAHL, PsycINFO) were searched starting at the time of establishment of the database to May 2023. Following the PRISMAs and use Review Manager 5.3 software for meta-analysis. Two review authors independently assessed the quality of the studies using the risk of bias 2 tool. The protocol was registered in PROSPERO (CRD42023410536).

Results

The search strategy obtained 1675 references. After abstract and full text screening, a total of seven studies were included. Shared decision-making interventions include decision aids and counselling that can help pregnant women analyse the pros and cons of various options and help them make decisions that are consistent with their values. The pooled results showed that shared decision-making intervention alleviated decisional conflicts regarding mode of delivery after caesarean section, but had no effect on knowledge and informed choice.

Conclusion

The results of our review suggest that shared decision-making is an effective intervention to improve the quality of decision-making about the mode of delivery of pregnant women after caesarean section. However, due to the low quality of the evidence, it is recommended that more studies be conducted in the future to improve the quality of the evidence.

Correlation with Clinical Practice

This systematic review and meta-analysis provides evidence for the effectiveness of shared decision-making for mode of delivery after cesarean section and may provide a basis for the development of intervention to promote the participation of pregnant women in the decision-making process.