Image quality assessment is essential for retinopathy detection on color fundus retinal image. However, most studies focused on the classification of good and poor quality without considering the different types of poor quality. This study developed an automatic retinal image analysis (ARIA) method, incorporating transfer net ResNet50 deep network with the automatic features generation approach to automatically assess image quality, and distinguish eye-abnormality-associated-poor-quality from artefact-associated-poor-quality on color fundus retinal images. A total of 2434 retinal images, including 1439 good quality and 995 poor quality (483 eye-abnormality-associated-poor-quality and 512 artefact-associated-poor-quality), were used for training, testing, and 10-ford cross-validation. We also analyzed the external validation with the clinical diagnosis of eye abnormality as the reference standard to evaluate the performance of the method. The sensitivity, specificity, and accuracy for testing good quality against poor quality were 98.0%, 99.1%, and 98.6%, and for differentiating between eye-abnormality-associated-poor-quality and artefact-associated-poor-quality were 92.2%, 93.8%, and 93.0%, respectively. In external validation, our method achieved an area under the ROC curve of 0.997 for the overall quality classification and 0.915 for the classification of two types of poor quality. The proposed approach, ARIA, showed good performance in testing, 10-fold cross validation and external validation. This study provides a novel angle for image quality screening based on the different poor quality types and corresponding dealing methods. It suggested that the ARIA can be used as a screening tool in the preliminary stage of retinopathy grading by telemedicine or artificial intelligence analysis.

AIM: To assess the efficacy of ketotifen in treating allergic conjunctivitis. METHODS: A systematic search of systematic reviews and Meta-analyses was conducted on the PubMed and Web Science of Science until October 2021 to address this knowledge gap. Mean difference with 95%CI and P values were used to assess the efficacy of ketotifen. The heterogeneity (I2) was used to evaluate the impact of heterogeneity. RESULTS: Eight randomized controlled trials (RCTs) involving 1589 patients were included in this Meta-analysis. The results revealed that after treating with ketotifen, itching (MD=-0.91, 95%CI: -1.63 to -0.20, I2=94%, P=0.01), tearing (MD=-0.40, 95%CI: -0.61 to -0.18, I2=75%, P=0.0003) and total signs and symptoms (MD=-0.85, 95%CI: -1.12 to -0.58, I2=0, P<0.00001) showed better benefit effect compared to the placebo group. CONCLUSION: Topical ketotifen is an effective treatment for patients with allergic conjunctivitis.