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Retinal blood vessels are the only directly observed blood vessels in the body; changes in them can help effective assess the occurrence and development of ocular and systemic diseases. The specificity and efficiency of retinal vessel quantification technology has improved with the advancement of retinal imaging technologies and artificial intelligence (AI) algorithms; it has garnered attention in clinical research and applications for the diagnosis and treatment of common eye and related systemic diseases. A few articles have reviewed this topic; however, a summary of recent research progress in the field is still needed. This article aimed to provide a comprehensive review of the research and applications of retinal vessel quantification technology in ocular and systemic diseases, which could update clinicians and researchers on the recent progress in this field.
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Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.
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Varicella-zoster virus (VZV) infections result in a series of ophthalmic complications. Clinically, we also discover that the proportion of dry eye symptoms was significantly higher in patients with herpes zoster ophthalmicus (HZO) than in healthy individuals. Meibomian gland dysfunction (MGD) is one of the main reasons for dry eye. Therefore, we hypothesize that HZO may associate with MGD, affecting the morphology of meibomian gland (MG) because of immune response and inflammation. The purpose of this study is to retrospectively analyze the effect of HZO with craniofacial herpes zoster on dry eye and MG morphology based on an Artificial intelligence (AI) MG morphology analytic system. In this study, 26 patients were diagnosed as HZO based on a history of craniofacial herpes zoster accompanied by abnormal ocular signs. We found that the average height of all MGs of the upper eyelid and both eyelids were significantly lower in the research group than in the normal control group (p < 0.05 for all). The average width and tortuosity of all MGs for both upper and lower eyelids were not significantly different between the two groups. The MG density of the upper eyelid and both eyelids were significantly lower in the HZO group than in the normal control group (p = 0.020 and p = 0.022). Therefore, HZO may lead to dry eye, coupled with the morphological changes of MGs, mainly including a reduction in MG density and height. Moreover, it is important to control HZO early and timely, which could prevent potential long-term severe ocular surface injury.
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Purpose: To assess the value of an automated classification model for dry and wet macular degeneration based on the ConvNeXT model. Methods: A total of 672 fundus images of normal, dry, and wet macular degeneration were collected from the Affiliated Eye Hospital of Nanjing Medical University and the fundus images of dry macular degeneration were expanded. The ConvNeXT three-category model was trained on the original and expanded datasets, and compared to the results of the VGG16, ResNet18, ResNet50, EfficientNetB7, and RegNet three-category models. A total of 289 fundus images were used to test the models, and the classification results of the models on different datasets were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), accuracy, and kappa. Results: Using 289 fundus images, three-category models trained on the original and expanded datasets were assessed. The ConvNeXT model trained on the expanded dataset was the most effective, with a diagnostic accuracy of 96.89%, kappa value of 94.99%, and high diagnostic consistency. The sensitivity, specificity, F1-score, and AUC values for normal fundus images were 100.00, 99.41, 99.59, and 99.80%, respectively. The sensitivity, specificity, F1-score, and AUC values for dry macular degeneration diagnosis were 87.50, 98.76, 90.32, and 97.10%, respectively. The sensitivity, specificity, F1-score, and AUC values for wet macular degeneration diagnosis were 97.52, 97.02, 96.72, and 99.10%, respectively. Conclusion: The ConvNeXT-based category model for dry and wet macular degeneration automatically identified dry and wet macular degeneration, aiding rapid, and accurate clinical diagnosis.