Eye and Vision最新文献

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 .

Background: To assess clinical outcomes after implanting toric, extended-depth-of-focus intraocular lenses (IOLs) to correct low corneal astigmatism in eyes with cataracts.

Methods: 47 eyes were implanted with the AcrySof IQ Vivity Toric DFT215 IOL. Main outcome measures were refractive error, monocular uncorrected and corrected distance (UDVA/CDVA), uncorrected and distance-corrected intermediate (UIVA/DCIVA), and uncorrected near and distance-corrected near (UNVA/DCNVA) visual acuities, monocular defocus curve, rotational stability, and IOLSAT and QUVID questionnaires. Patients were assessed at 3 months postsurgery.

Results: All eyes had a postoperative spherical equivalent (SE) within ± 0.50 D and 97.87% (n = 46) had a refractive cylinder ≤ 0.50 D. The mean SE and refractive cylinder were - 0.10 ± 0.17 D and - 0.16 ± 0.24 D, respectively. The CDVA was ≥ 20/25 and ≥ 20/32 in 95.74% (n = 45) and 97.87% (n = 46) of eyes, respectively. The DCIVA was ≥ 20/32 in 85.11% (n = 40) of eyes and the DCNVA was ≥ 20/40 in 74.47% (n = 35). The mean values of CDVA, DCIVA, and DCNVA were - 0.02 ± 0.08, 0.14 ± 0.09, and 0.23 ± 0.12 logMAR, respectively. The defocus curve revealed good visual acuity at far and intermediate distances with a depth-of-focus of about 1.75 D. IOL rotation was 0.74 ± 1.13 degrees and all eyes had a rotation of less than 5 degrees. Patients reported either good or very good postoperative vision without eyeglasses under bright-light-conditions at distance (87.80%, 36/41) and intermediate distance (92.68%, 38/41). Between about 63.83%-72.34% (30-34) of patients reported no starburst, halos, or glare, or if experienced, were not bothersome.

Conclusions: The Vivity toric IOL implanted in eyes with low-astigmatism provides accurate refractive outcomes, good visual acuity at different distances and excellent rotational stability. Trial Registration The study was registered with the German Clinical Trials Register (DRKS00030579).

Background: Keratoconus is an ectatic, progressive corneal disorder characterized by alterations in the morphology of the corneal tissue that leads to limitation of visual function of the patient. Intracorneal ring segments (ICRS) are small synthetic devices that are implanted in the corneal stromal in order to regularize the morphology of the tissue therefore improving the visual function and the quality of life of the patients.

Main text: The present narrative review summarizes the main scientific articles developed by the authors in relation to the clinical outcomes and long-term results of ICRS in the treatment of keratoconus. It was found that those patients that benefit the most from this surgical intervention are those that have the most severe form of keratoconus. Additionally, patients with good visual function, those with more than 0.9 in the decimal scale are at risk of losing visual acuity after ICRS implantation. In relation to long-term results, scientific investigations published by the authors demonstrate that ICRS is a stable procedure after long period of time in terms of vision, refraction, and topographic variables in those patients with stable keratoconus. However, in patients with keratoconus and signs of progression, ICRS may not have the capability of halting the progression of the disease. Using artificial intelligence to guide ICRS implantation provide better clinical outcomes and improvement in corneal higher-order aberrations in patients with keratoconus in comparison to those treated using the commercial nomogram of implantation.

Conclusions: ICRS is a safe surgical procedure in the treatment of keratoconus. Patients that benefit most from the surgery are those with a significant visual impairment. ICRS should not be considered in patients with good visual function because of the risk of losing lines of vision. Long-term follow-up demonstrate stability of the clinical outcomes in patients with stable keratoconus although ICRS may not have the ability of halting the progression of the disease. New technologies based artificial intelligence improved the indications and the clinical outcomes of keratoconus patients treated with ICRS.

Background: To describe the diagnostic performance of a deep learning (DL) algorithm in detecting Fuchs endothelial corneal dystrophy (FECD) based on specular microscopy (SM) and to reliably detect widefield peripheral SM images with an endothelial cell density (ECD) > 1000 cells/mm².

Methods: Five hundred and forty-seven subjects had SM imaging performed for the central cornea endothelium. One hundred and seventy-three images had FECD, while 602 images had other diagnoses. Using fivefold cross-validation on the dataset containing 775 central SM images combined with ECD, coefficient of variation (CV) and hexagonal endothelial cell ratio (HEX), the first DL model was trained to discriminate FECD from other images and was further tested on an external set of 180 images. In eyes with FECD, a separate DL model was trained with 753 central/paracentral SM images to detect SM with ECD > 1000 cells/mm² and tested on 557 peripheral SM images. Area under curve (AUC), sensitivity and specificity were evaluated.

Results: The first model achieved an AUC of 0.96 with 0.91 sensitivity and 0.91 specificity in detecting FECD from other images. With an external validation set, the model achieved an AUC of 0.77, with a sensitivity of 0.69 and specificity of 0.68 in differentiating FECD from other diagnoses. The second model achieved an AUC of 0.88 with 0.79 sensitivity and 0.78 specificity in detecting peripheral SM images with ECD > 1000 cells/mm².

Conclusions: Our pilot study developed a DL model that could reliably detect FECD from other SM images and identify widefield SM images with ECD > 1000 cells/mm² in eyes with FECD. This could be the foundation for future DL models to track progression of eyes with FECD and identify candidates suitable for therapies such as Descemet stripping only.