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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.

IntroductionPathologic myopia (PM) is a leading cause of irreversible blindness worldwide, yet effective therapeutic interventions remain limited. Although repeated low-level red light (RLRL) therapy has demonstrated significant efficacy in controlling myopia progression among children, its application in patients with PM remains unexplored. This study aims to evaluate the regulatory effects of RLRL on choroidal vascular density and thickness in patients with PM and verify its safety and efficacy in delaying the progression of fundus atrophic lesions.Methods and analysisThis is a prospective, randomised, controlled trial enrolling 158 patients with PM aged 18–55 years. Participants will be randomised (1:1) to an intervention group or a control group. The intervention group will receive light-emitting diode–based RLRL therapy (wavelength, 660 nm; power density, 65 mW/cm²) administered two times daily for 3 min per session, 5 days per week, over a 12-month period. The control group will receive a sham treatment (power density, 5 mW/cm²) following an identical schedule. The primary outcome measure is the change in choroidal vascular density and choroidal vessel volume index at 12 months.Ethics and disseminationThis study has been approved by the Ethics Review Committee of Shanghai Eye Disease Prevention and Treatment Centre (EC-20250506-04). This study will be conducted in adherence to the approved protocol, Good Clinical Practice and the Declaration of Helsinki. The study results will be submitted to a peer-reviewed journal and presented at both local and international congresses.Trial registration numberNCT07312214

This reply addresses Chan et al.’s comments on our previous study, clarifying the use of foundation models (e.g., RETFound), commercial data sources/anonymity, and DeLong test results. We highlight that the SDEDS-fine-tuned RETFound outperforms commercial models and acknowledge the need for further testing, while referencing an early benchmark value.

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.

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.

ObjectivesThis study aims to detect characteristic fundus changes in pathological myopia using deep learning (DL)-based analysis of ultra-widefield (UWF) fundus imaging.MethodsFollowing the exclusion of low-quality images, this cross-sectional study used 1105 UWF images from 543 patients with high myopia to develop the model, along with 293 images from 150 patients with high myopia for external testing. All images were retrospectively collected from patients with high myopia at Shanghai General Hospital and Shanghai Eye Diseases Prevention and Treatment Center between 2018 and 2024. We trained a DL model based on an ophthalmology foundational model to detect myopic maculopathy (MM) and posterior staphyloma (PS).ResultsThe proposed RETFound-enhanced model demonstrated robust performance. For five-category classification of MM, it achieved 65.4% accuracy and an F1 score of 0.648, outperforming other methods. In three-category MM classification, it achieved 79.4% accuracy and an F1 score of 0.793. For PS detection, the model reached 84.1% accuracy, an F1 score of 0.814 and an area under the receiver operating characteristic curve (AUROC) of 0.886, highlighting its effectiveness as a screening tool. External validation showed consistent performance, with 64.4% accuracy for five-category MM classification, 79.8% accuracy for three-category classification and 81.2% accuracy for PS, confirming robustness across cohorts.ConclusionsThis study presents an effective diagnostic model for pathological myopia using UWF fundus imaging and a foundation model. The integration of DL with non-mydriatic UWF fundus imaging demonstrates promising potential for applications in primary healthcare, particularly in underserved areas, enabling accessible screening for high myopia-related fundus changes.

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.

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.

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; = 99%), and an increased prevalence of high myopia (OR = 3.36; 95% CI = 1.26, 9.00; = 96%). Shorter sleep duration was significantly linked to faster changes in axial length (AL) (β = 0.05; 95% CI = 0.02, 0.08; = 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.

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.