Eye and Vision最新文献

Until recently, corneal topography has been the gold standard in detecting keratectasia and monitoring its progression. The recently introduced ABCD tomographic keratoconus staging system focuses on anterior ("A") and posterior ("B") radius of curvature, thinnest corneal thickness ("C"), best-corrected visual acuity with spectacles ("D") and is supplemented with the introduction of the biomechanical E-staging (BEST, "E"). The need for biomechanical staging arose from the fact of altered biomechanical characteristics of keratectasia in comparison to healthy corneas. Ectatic corneas usually exhibit a biomechanical weakening and greater deformation than healthy corneas when exposed to a biomechanical stressor such as a standardized air puff indentation as provided by the Corvis ST® (CST, Oculus, Wetzlar, Germany). The BEST is based on the linear term of the Corvis Biomechanical Index (CBI) and provides a biomechanical keratoconus severity staging and progression assessment within the CST software. This review traces the development of the BEST as an addition to the tomographic ABCD staging system and highlights its strengths and limitations when applied in daily practice for the detection, monitoring and progression assessment in keratectasia.

Background: Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges.

Main text: This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications.

Conclusion: AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

Background: Dry eyes can cause discomfort. To treat dry eye disease, cyclosporine A (CsA) and Lifitegrast are two eye drugs approved by the U.S. Food and Drug Administration (FDA). However, frequent use of eye drops can be challenging and lead to poor compliance, especially in elderly patients. Therefore, this study aimed to develop a drug sustained-release vector and explore its therapeutic effect in animal models of dry eye.

Methods: Firstly, drug membranes loaded with both CsA and Lifitegrast using a carrier called poly(lactate-co-ε-caprolactone) (P(LLA-CL)) were prepared and evaluated for their physicochemical properties, release behavior in vitro, and safety in vivo. Next, a rabbit dry eye model using a 0.1% benzalkonium chloride (BAC) solution was developed and treated by drug-loaded micro membranes. We observed and recorded conjunctival hyperemia, corneal staining, corneal edema, corneal neovascularization, conjunctival goblet cells and hematoxylin and eosin (H&E) staining. Finally, we detected the MUC5AC and MMP-9 by immunofluorescence staining and enzyme-linked immunosorbent assay (ELISA).

Results: The composite film released both CsA and Lifitegrast for at least one month. Compared to the blank membrane group, conjunctival hyperemia, corneal fluorescein staining, corneal edema, corneal neovascularization and conjunctival goblet cells recovered faster in the drug membrane group, and the difference was statistically significant. At the molecular level, the drug membrane group showed an increase in mucin density and a significant anti-inflammatory effect.

Conclusions: The implantation of CsA/Lifitegrast loaded P(LLA-CL) membrane under the subconjunctival of the rabbit eye is safe. The study suggests that this subconjunctival administration could be developed into a minimally invasive delivery system to help patients with dry eye disease who require multiple daily eyedrops but have poor compliance.

Background: Myopia affects 1.4 billion individuals worldwide. Notably, there is increasing evidence that choroidal thickness plays an important role in myopia and risk of developing myopia-related conditions. With the advancements in artificial intelligence (AI), choroidal thickness segmentation can now be automated, offering inherent advantages such as better repeatability, reduced grader variability, and less reliance for manpower. Hence, we aimed to evaluate the agreement between AI-automated and manual segmented measurements of subfoveal choroidal thickness (SFCT) using two swept-source optical coherence tomography (OCT) systems.

Methods: Subjects aged ≥ 16 years, with myopia of ≥ 0.50 diopters in both eyes, were recruited from the Prospective Myopia Cohort Study in Singapore (PROMYSE). OCT scans were acquired using Triton DRI-OCT and PLEX Elite 9000. OCT images were segmented both automatically with an established SA-Net architecture and manually using a standard technique with adjudication by two independent graders. SFCT was subsequently determined based on the segmentation. The Bland-Altman plot and intraclass correlation coefficient (ICC) were used to evaluate the agreement.

Results: A total of 229 subjects (456 eyes) with mean [± standard deviation (SD)] age of 34.1 (10.4) years were included. The overall SFCT (mean ± SD) based on manual segmentation was 216.9 ± 82.7 µm with Triton DRI-OCT and 239.3 ± 84.3 µm with PLEX Elite 9000. ICC values demonstrated excellent agreement between AI-automated and manual segmented SFCT measurements (PLEX Elite 9000: ICC = 0.937, 95% CI: 0.922 to 0.949, P < 0.001; Triton DRI-OCT: ICC = 0.887, 95% CI: 0.608 to 0.950, P < 0.001). For PLEX Elite 9000, manual segmented measurements were generally thicker when compared to AI-automated segmented measurements, with a fixed bias of 6.3 µm (95% CI: 3.8 to 8.9, P < 0.001) and proportional bias of 0.120 (P < 0.001). On the other hand, manual segmented measurements were comparatively thinner than AI-automated segmented measurements for Triton DRI-OCT, with a fixed bias of - 26.7 µm (95% CI: - 29.7 to - 23.7, P < 0.001) and proportional bias of - 0.090 (P < 0.001).

Conclusion: We observed an excellent agreement in choroidal segmentation measurements when comparing manual with AI-automated techniques, using images from two SS-OCT systems. Given its edge over manual segmentation, automated segmentation may potentially emerge as the primary method of choroidal thickness measurement in the future.

The cornea, consisting of three cellular and two non-cellular layers, is the outermost part of the eyeball and frequently injured by external physical, chemical, and microbial insults. The epithelial-to-mesenchymal transition (EMT) plays a crucial role in the repair of corneal injuries. Zinc finger E-box binding homeobox 1 (ZEB1), an important transcription factor involved in EMT, is expressed in the corneal tissues. It regulates cell activities like migration, transformation, and proliferation, and thereby affects tissue inflammation, fibrosis, tumor metastasis, and necrosis by mediating various major signaling pathways, including transforming growth factor (TGF)-β. Dysfunction of ZEB1 would impair corneal tissue repair leading to epithelial healing delay, interstitial fibrosis, neovascularization, and squamous cell metaplasia. Understanding the mechanism underlying ZEB1 regulation of corneal injury repair will help us to formulate a therapeutic approach to enhance corneal injury repair.

Background: To evaluate the therapeutic effects of topical RCI001 (RCI) and compare its efficacy with that of 1% prednisolone acetate (PDE) and 5% Lifitegrast in a modified mixed dry eye disease (DED) model.

Methods: The environmental DED model was induced in BALB/c mice in a dry chamber with scopolamine. The eyes of the mice were treated topically with phosphate buffered saline (PBS), PDE, Lifitegrast or RCI twice daily for 1 week. Ocular surface staining (OSS), tear secretion, inflammatory cytokines in the ocular surface and lacrimal gland, and immunofluorescence staining in the conjunctiva and cornea(CC) were assessed.

Results: The RCI group demonstrated better improvement of OSS and tear secretion than the PBS group (OSS, PBS: 13.0 ± 1.6, RCI: 9.4 ± 3.0; tear secretion, PBS: 5.0 ± 0.4 mm, RCI: 7.0 ± 0.3 mm, each P < 0.001) and better clinical efficacy than PDE and Lifitegrast groups on Day 7 (improvement rate of OSS, RCI: 32.45%, Lifitegrast: 13.13%, PDE: 12.25%). The RCI group resulted in significantly lower expression of oxidative stress markers in the CC than the PBS group (4-HNE, NOX2, and NOX4 in the conjunctiva; NOX2 in the cornea, each P < 0.05). However, the PDE and Lifitegrast groups did not show significant differences compared with the PBS group. There were no significant differences of inflammatory cytokines in the ocular surface and lacrimal gland between all groups.

Conclusion: Topical RCI001 showed excellent therapeutic effects in environmental DED models by stimulating tear secretion, modulating oxidative stress and improving corneal epithelial healing compared to 1% PDE and 5% Lifitegrast.

Keratoconus is a common progressive corneal disorder that can be associated with significant ocular morbidity. Various corneal imaging techniques have been used for the diagnosis of established cases. However, in the early stages of the disease, which include subclinical keratoconus and forme fruste keratoconus, detection of such cases can be challenging. The importance of detecting such cases is very important because early intervention can halt disease progression, improve visual outcomes and prevent postrefractive surgery ectasia associated with performing corneal refractive procedures in such patients. This narrative review aimed to examine several established and evolving imaging techniques for the detection of early cases of keratoconus. The utilization of combinations of these techniques may further increase their diagnostic ability.

Background: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

Main text: This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care.

Conclusion: AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Background: Near work is generally considered as a risk factor for myopia onset and progression. This study aimed to investigate the choroidal responses to a brief-period of near work in children and young adults.

Methods: Thirty myopic medical students (aged 18-28 years) and 30 myopic children (aged 8-12 years) participated in this study. The submacular total choroidal area (TCA), luminal area (LA), stromal area (SA), choroidal vascularity index (CVI) and choriocapillaris flow deficit (CcFD), as well as subfoveal choroidal thickness (SFCT) were measured with swept-source optical coherence tomography/optical coherence tomography angiography (SS-OCT/OCTA) before and immediately after 20 min, 40 min, 60 min of near work at a distance of 33 cm.

Results: In adults, 20 min of near work induced a significant reduction in SFCT (- 5.1 ± 6.5 μm), LA [(- 19.2 ± 18.6) × 10³ μm²], SA [(- 8.2 ± 12.6) × 10³ μm²] and TCA [(- 27.4 ± 24.9) × 10³ μm²] (all P < 0.01). After 40 min of near work, LA was still reduced [(- 9.4 ± 18.3) × 10³ μm²], accompanied with a decreased CVI (- 0.39% ± 0.70%) and an increased CcFD (0.30% ± 0.78%) (all P < 0.05). After 60 min of near work, CVI was still reduced (- 0.28% ± 0.59%), and CcFD was still increased (0.37% ± 0.75%) (all P < 0.05). In children, 20 min of near work induced a significant increase in CcFD (0.55% ± 0.64%), while 60 min of near work induced increases in SA [(7.2 ± 13.0) × 10³ μm²] and TCA [(9.7 ± 25.3) × 10³ μm²] and a reduction in CVI (- 0.28% ± 0.72%) (all P < 0.05). Children exhibited lower near work-induced LA and TCA reduction than adults, with a mean difference of - 0.86% and - 0.82%, respectively (all P < 0.05).

Conclusions: The temporal characteristics and magnitude of changes of choroidal vascularity and choriocapillaris perfusion during near work was not identical between children and adults. The initial response to near work was observed in choriocapillaris in children, whereas it was observed in the medium- and large-sized vessels in adults.

Trial registration: Clinical Trial Registry (ChiCTR), ChiCTR2000040205. Registered on 25 November 2020, https://www.chictr.org.cn/bin/project/edit?pid=64501 .