Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting.

PURPOSE: This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators. DESIGN: Prospective cross-sectional study. METHODS: In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography. RESULTS: There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above. CONCLUSIONS: The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.

Insights into artificial intelligence in myopia management: from a data perspective.

Given the high incidence and prevalence of myopia, the current healthcare system is struggling to handle the task of myopia management, which is worsened by home quarantine during the ongoing COVID-19 pandemic. The utilization of artificial intelligence (AI) in ophthalmology is thriving, yet not enough in myopia. AI can serve as a solution for the myopia pandemic, with application potential in early identification, risk stratification, progression prediction, and timely intervention. The datasets used for developing AI models are the foundation and determine the upper limit of performance. Data generated from clinical practice in managing myopia can be categorized into clinical data and imaging data, and different AI methods can be used for analysis. In this review, we comprehensively review the current application status of AI in myopia with an emphasis on data modalities used for developing AI models. We propose that establishing large public datasets with high quality, enhancing the model's capability of handling multimodal input, and exploring novel data modalities could be of great significance for the further application of AI for myopia.

Effects of Insufficient Sleep on Myopia in Children: A Systematic Review and Meta-Analysis.

Myopia is increasingly prevalent in children. Its association with insufficient sleep has been studied, yielding inconsistent findings. This review aims to assess the association of insufficient sleep with myopia and myopia-related refractive parameters in children. A total of 657 articles were identified, of which 40 were included in the systematic review and 33 were included in the meta-analysis. Results showed that insufficient sleep was significantly associated with an increased prevalence of myopia (odds ratio [OR] = 1.59; 95% confidence interval [CI] = 1.31, 1.95; I 2 = 99%), and an increased prevalence of high myopia (OR = 3.36; 95% CI = 1.26, 9.00; I 2 = 96%). Shorter sleep duration was significantly linked to faster changes in axial length (AL) (ß = 0.05; 95% CI = 0.02, 0.08; I 2 = 0%). However, correlation between insufficient sleep and the incidence of myopia, spherical equivalent refraction, corneal curvature radius (CR) and AL/CR were insignificant. Moreover, the effect of insufficient sleep on premyopia and astigmatism was not well-studied. The results of this study suggest that insufficient sleep may be an important risk factor for the development of myopia in school-aged children. Therefore, in addition to ensuring sufficient outdoor activities and reducing near work, it is necessary to inform children and parents about the importance of adequate sleep to mitigate the risk of myopia.

Self-supervised learning-enhanced deep learning method for identifying myopic maculopathy in high myopia patients.

Accurate detection and timely care for patients with high myopia present significant challenges. We developed a deep learning (DL) system enhanced by a self-supervised learning (SSL) approach to improve the automatic diagnosis of myopic maculopathy (MM). Using a dataset of 7,906 images from the Shanghai High Myopia Screening Project and a public validation set of 1,391 images from MMAC2023, our method significantly outperformed conventional techniques. Internally, it achieved 96.8% accuracy, 83.1% sensitivity, and 95.6% specificity, with AUC values of 0.982 and 0.999. Externally, it maintained 89.0% accuracy, 71.7% sensitivity, and 87.8% specificity, with AUC values of 0.978 and 0.973. The model's Cohen's kappa values exceeded 0.8, indicating substantial agreement with retinal experts. Our SSL-enhanced DL approach offers high accuracy and potential to enhance large-scale myopia screenings, demonstrating broader significance in improving early detection and treatment of MM.

Sleep onset time as a mediator in the association between screen exposure and aging: a cross-sectional study.

Excessive screen exposure has become a significant health concern. This study investigates the impact of screen time on aging in middle-aged and elderly populations. Healthy working adults over 45 years old in Shanghai, China, underwent general and ocular examinations. Questionnaires collected demographics, medical history, and screen exposure details. Aging was assessed using the retinal age gap, defined as the difference between the retinal age predicted by deep learning algorithms based on fundus images and chronological age. Pathway analysis tested the mediation effect of sleep duration and onset time on the relationship between screen usage and retinal age gap. The retinal age gap increased with longer screen exposure, from 0.49 ± 3.51 years in the lowest tertile to 5.13 ± 4.96 years in the highest tertile (Jonckheere-Terpstra test, p < 0.001). Each additional hour of screen exposure accelerated the retinal age gap by 0.087 years (95% CI, 0.027, 0.148, p = 0.005) in the fully adjusted linear model. Sleep onset time mediated the impact of screen usage on the retinal age gap (indirect effect, ß = 0.11; 95% CI 0.04-0.24). The impact of screen usage in a light-off environment on the retinal age gap was fully mediated by sleep onset time (indirect effect, ß = 0.22; 95% CI 0.07-0.38), with the proportion being 100%. Our study identified a correlation between excessive screen time and a wider retinal age gap in middle-aged and elderly individuals, likely due to delayed sleep onset. To mitigate the adverse effects on the retina and aging, it is important to limit screen usage and avoid screens before bedtime.

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis.

Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.

Artificial intelligence technology for myopia challenges: A review.

Myopia is a significant global health concern and affects human visual function, resulting in blurred vision at a distance. There are still many unsolved challenges in this field that require the help of new technologies. Currently, artificial intelligence (AI) technology is dominating medical image and data analysis and has been introduced to address challenges in the clinical practice of many ocular diseases. AI research in myopia is still in its early stages. Understanding the strengths and limitations of each AI method in specific tasks of myopia could be of great value and might help us to choose appropriate approaches for different tasks. This article reviews and elaborates on the technical details of AI methods applied for myopia risk prediction, screening and diagnosis, pathogenesis, and treatment.

Deep learning-based analysis of infrared fundus photography for automated diagnosis of diabetic retinopathy with cataracts.

PURPOSE: To develop deep learning-based networks for the diagnosis of diabetic retinopathy (DR) with cataracts based on infrared fundus images. SETTING: Shanghai General Hospital, Shanghai Eye Disease Prevention & Treatment Center, Shanghai, China. DESIGN: Development and evaluation of an artificial intelligence (AI) diagnostic method. METHODS: A total of 10 665 infrared fundus images from 4553 patients with diabetes were used to train and test the model. For image quality assessment, left and right eye classification, DR diagnosis and grading, and segmentation of 3 DR lesions, an end-to-end software using EfficientNet and UNet was developed. The accuracy and performance of the software in comparison to human experts was evaluated. RESULTS: The model achieved an accuracy of 75.31% for left and right eye classification, 100% for DR grading and diagnosis tasks, and 73.67% for internal test set, with corresponding areas under the curve (AUCs) of 0.88, 1.00, and 0.89, respectively. For DR lesion segmentation, the AUCs of hemorrhagic, microangioma, and exudative lesions were 0.86, 0.66, and 0.84, respectively. In addition, a contrast test of human-machine film reading confirmed the software's high sensitivity (96.3%) and specificity (90.0%) and consistency with the manual film reading group (κ = 0.869, P < .001). This easily deployable software generated reports quickly and promoted efficient DR screening with cataracts in clinical and community settings. CONCLUSIONS: AI-assisted software can perform automatic analysis of infrared fundus images and has substantial application value for the diagnosis of DR patients with cataracts.

Defining the Potential Targets for Biological Activity of Isoegomaketone Based on Network Pharmacology and Molecular Docking Methods.

Isoegomaketone is a water-soluble natural ketone compound that is commonly present in Rabdosia angustifolia and Perilla frutescens. At present, it is known that isoegomaketone has a wide range of pharmacological activity, but there has been no thorough investigation of its potential targets. As a result, we examined the potential targets of isoegomaketone using the network pharmacology approach. In our study, the TCM Database@Taiwan was utilized to search for the chemical formula. The pharmacological characteristics of isoegomaketone were then evaluated in silico using the Swiss Absorption, Distribution, Metabolism, and Excretion (Swiss ADME) and Deep Learning-Acute Oral Toxicity (DL-AOT) methods, and the potential isoegomaketone target genes were identified using a literature study. Additionally, using the clusterProfiler R package 3.8.1, the Gene Ontology (GO) enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of target genes were performed. In order to obtain the protein interaction network, we simultaneously submitted the targets to the STRING database. After this, we performed molecular docking with respect to targets and isoegomaketone. Finally, we created visual networks of protein-protein interactions (PPI) and examined these networks. Our results showed that isoegomaketone had good drug-likeness, bioavailability, medicinal chemistry friendliness, and acceptable toxicity. Subsequently, through the literature analysis, 48 target genes were selected. The bioinformatics analysis and network analysis found that these target genes were closely related to the biological processes of isoegomaketone, such as atherosclerotic formation, inflammation, tumor formation, cytotoxicity, bacterial infection, virus infection, and parasite infection. These findings show that isoegomaketone may interact with a wide range of proteins and biochemical processes to form a systematic pharmacological network, which has good value for the creation and use of drugs.

Advances in the Pharmacological Activities and Effects of Perilla Ketone and Isoegomaketone.

As components of a traditional Chinese herbal medicine with many physiological activities, perilla ketone and isoegomaketone isolated from perilla essential oil are important active components of Perilla frutescens. Recent studies have shown that these two compounds have promising antitumor, antifungal, antirheumatoid arthritis, antiobesity, anti-inflammatory, healing-promoting, and other activities and can be used to combat toxicity from immunotherapy. Therefore, the multitude of pharmacological activities and effects demonstrate the broad research potential of perilla ketone and isoegomaketone. However, no reviews have been published related to the pharmacological activities or effects of perilla ketone and isoegomaketone. The purpose of this review is as follows: (1) outline the recent advances made in understanding the pharmacological activities of perilla ketone and isoegomaketone; (2) summarize their effects; and (3) discuss future research perspectives.