Eye and Vision最新文献

Background: Atropine, specifically 0.05% eyedrops, has proven effective in slowing myopia progression. This study aims to investigate peripheral refraction (PR) characteristics in myopic children treated with 0.05% atropine eyedrops at different frequencies.

Methods: One hundred thirty-eight myopic children completed this one-year prospective study, randomly assigned to once daily (7/7), twice per week (2/7), or once per week (1/7) groups. Spherical equivalent (SE) and axial length (AL) were measured. PR was assessed using a custom-made Hartmann-Shack wavefront peripheral sensor, covering a visual field of horizontal 60° and vertical 36°. Relative peripheral refraction (RPR) was calculated by subtracting central from peripheral measurements.

Results: After one year, SE increased more significantly in the 1/7 group compared to the 7/7 group (P < 0.001) and 2/7 group (P = 0.004); AL elongation was also greater in the 1/7 group compared to the 7/7 group (P < 0.001). In comparison with higher frequency groups, 1/7 group exhibited more myopic PR in the fovea and its vertical superior, inferior, and nasal retina; and less myopic RPR in the periphery retina after one-year (P < 0.05). Additionally, RPR in the 7/7 group demonstrated myopic shift across the entire retina, the 2/7 group in temporal and inferior retina, while the 1/7 group showed a hyperopic shift in the superior retina (P < 0.05). Moreover, myopic shift of RPR in the temporal retina is related to less myopia progression, notably in the 7/7 group (P < 0.05).

Conclusions: Atropine inhibits myopia progression in a frequency-dependent manner. The once-daily group showed the slowest myopia progression but exhibited more myopic shifts in RPR. Additionally, RPR in the temporal retina was related to myopia progression in all groups.

Trial registration: Chinese Clinical Trial Registry, ChiCTR2100043506. Registered 21 February 2021, https://www.chictr.org.cn/showproj.html?proj=122214.

Background: Acute retinal necrosis (ARN) is a relatively rare but highly damaging and potentially sight-threatening type of uveitis caused by infection with the human herpesvirus. Without timely diagnosis and appropriate treatment, ARN can lead to severe vision loss. We aimed to develop a deep learning framework to distinguish ARN from other types of intermediate, posterior, and panuveitis using ultra-widefield color fundus photography (UWFCFP).

Methods: We conducted a two-center retrospective discovery and validation study to develop and validate a deep learning model called DeepDrARN for automatic uveitis detection and differentiation of ARN from other uveitis types using 11,508 UWFCFPs from 1,112 participants. Model performance was evaluated with the area under the receiver operating characteristic curve (AUROC), the area under the precision and recall curves (AUPR), sensitivity and specificity, and compared with seven ophthalmologists.

Results: DeepDrARN for uveitis screening achieved an AUROC of 0.996 (95% CI: 0.994-0.999) in the internal validation cohort and demonstrated good generalizability with an AUROC of 0.973 (95% CI: 0.956-0.990) in the external validation cohort. DeepDrARN also demonstrated excellent predictive ability in distinguishing ARN from other types of uveitis with AUROCs of 0.960 (95% CI: 0.943-0.977) and 0.971 (95% CI: 0.956-0.986) in the internal and external validation cohorts. DeepDrARN was also tested in the differentiation of ARN, non-ARN uveitis (NAU) and normal subjects, with sensitivities of 88.9% and 78.7% and specificities of 93.8% and 89.1% in the internal and external validation cohorts, respectively. The performance of DeepDrARN is comparable to that of ophthalmologists and even exceeds the average accuracy of seven ophthalmologists, showing an improvement of 6.57% in uveitis screening and 11.14% in ARN identification.

Conclusions: Our study demonstrates the feasibility of deep learning algorithms in enabling early detection, reducing treatment delays, and improving outcomes for ARN patients.

Background: To identify longitudinal changes in each retinal layer thickness in central retinal vein occlusion (CRVO) patients with resolved macular edema (ME).

Methods: In this retrospective observational study, CRVO patients without a recurrence of ME for more than 3 years and normal controls were enrolled. Each retinal layer thickness of the parafoveal area, including ganglion cell complex (GCC), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), photoreceptor layer (PRL), and retinal pigment epithelium (RPE) was measured. After the resolution of ME, three more examinations with a 1-year interval were analyzed.

Results: A total of 98 eyes were enrolled, 50 eyes for the control group and 48 eyes for the CRVO group. The baseline GCC thickness was 114.2 ± 15.6 μm and 104.2 ± 25.4 μm in the control and CRVO groups, respectively, which was significantly different (P = 0.022). The thicknesses of other layers including INL, OPL, ONL, PRL, and RPE were not significantly different at baseline. The reduction rate of GCC, INL, OPL, and ONL was - 3.92, - 1.33, - 0.91, and - 2.31 μm/year in the CRVO group, whereas no significant reductions were observed in the control group. Best-corrected visual acuity was significantly associated with changes in the GCC, OPL, and ONL in the CRVO group.

Conclusions: In patients with CRVO, even in the absence of recurrent ME, retinal damage progresses over time, evidenced by thinning of the inner retina and outer retina including OPL and ONL. These changes may be associated with alterations in visual function.

Background: Accurately assessing corneal structural status is challenging when thickness deviates from the average. Polarization-sensitive optical coherence tomography (PS-OCT) measures tissue-specific polarization changes, providing additional contrast for accurate segmentations and aids in phase retardation (PR) measurements. Previous studies have shown PR's effectiveness in identifying sub-clinical keratoconus (KC) in asymmetric cases. Thus, this study aims to assess PR distribution in thick corneas with and without KC.

Methods: In this retrospective and cross-sectional study, 45 thick corneas from 30 Asian-Indian subjects, categorized into healthy (n = 26) and KC (n = 19) groups were analyzed. All eyes underwent standard clinical evaluations, tomographic assessments, and corneal biomechanics measurements. PR and individual layer thicknesses were measured using custom-designed ultrahigh-resolution PS-OCT. PR en-face maps were generated. Individual layer thicknesses and PR analysis was conducted across multiple zones, extending up to 8-10 mm in diameter. All eyes in the study had not undergone interventions, received topical medications, or had previous corneal disease history.

Results: Significant differences were found in spherical and cylindrical powers, keratometry, pachymetry, and biomechanical indices (all P < 0.01). Thickness profiles from PS-OCT showed significant differences in the 4-8 mm zones only. Bowman's layer thickness significantly differed only in the central 2 mm zone (P = 0.02). The median PR values showed marginal differences in the central 2 mm zone (P = 0.0565). Additionally, there were significant differences observed in the 2-4 mm and 4-6 mm zones (P = 0.0274 and P = 0.0456, respectively). KC eyes exhibited an atypical PR distribution and corneal thinning, while normal eyes maintained a uniform Bowman's layer thickness and PR maps with larger areas of higher PR.

Conclusion: The study revealed distinctive PR distribution in thick corneas among healthy and KC groups. Using an ultrahigh-resolution PS-OCT the significance of Bowman's layer thickness in these groups was also emphasized. The study offered potential improvements in clinical diagnostics by enhancing our understanding of corneal structure and its altered function.

Background: This study proposes a decision support system created in collaboration with machine learning experts and ophthalmologists for detecting keratoconus (KC) severity. The system employs an ensemble machine model and minimal corneal measurements.

Methods: A clinical dataset is initially obtained from Pentacam corneal tomography imaging devices, which undergoes pre-processing and addresses imbalanced sampling through the application of an oversampling technique for minority classes. Subsequently, a combination of statistical methods, visual analysis, and expert input is employed to identify Pentacam indices most correlated with severity class labels. These selected features are then utilized to develop and validate three distinct machine learning models. The model exhibiting the most effective classification performance is integrated into a real-world web-based application and deployed on a web application server. This deployment facilitates evaluation of the proposed system, incorporating new data and considering relevant human factors related to the user experience.

Results: The performance of the developed system is experimentally evaluated, and the results revealed an overall accuracy of 98.62%, precision of 98.70%, recall of 98.62%, F1-score of 98.66%, and F2-score of 98.64%. The application's deployment also demonstrated precise and smooth end-to-end functionality.

Conclusion: The developed decision support system establishes a robust basis for subsequent assessment by ophthalmologists before potential deployment as a screening tool for keratoconus severity detection in a clinical setting.

Purpose: To assess rotational stability and refractive outcomes of a new toric hydrophobic acrylic intraocular lens (IOL).

Design: Single-center, prospective, interventional clinical trial.

Methods: A total of 130 eyes of 82 patients with age-related cataract and total corneal astigmatism of greater than 1.0 diopters (D) received a hydrophobic acrylic toric IOL Clareon CNW0T3-9. Baseline measurement for rotational stability evaluation was performed at the end of surgery (EOS), with the patient still supine on the operating table, using non-movable vessels as reference landmarks. Postoperative retroillumination pictures were taken at 1 h, 1 week, 1 month and 4-6 months postoperatively. Subjective manifest refraction was assessed at the 6 months follow-up visit.

Results: Final results were obtained in 126 eyes of 80 patients. Mean absolute rotation from EOS to 6 months was 1.33 ± 2.00 [0.01, 19.80] degrees. Rotational stability values from EOS to 1 h, 1 h to 1 week, 1 week to 1 month and 1 month to 6 months were 0.86 ± 0.82 [0.00, 3.90], 1.06 ± 1.94 [0.00, 19.45], 0.47 ± 0.42 [0.00, 2.03] and 0.38 ± 0.40 [0.00, 2.56] degrees. Mean preoperative corneal astigmatism was 1.78 ± 0.83 [1.00, 4.76] D which changed to a mean postoperative refractive astigmatism of 0.33 ± 0.27 [0.00, 1.25] D at 6 months.

Conclusion: The Clareon toric IOL presented very good rotational stability with a mean absolute rotation below 1.4° from EOS to 6 months. Only two IOLs rotated more than 5° with none of them requiring repositioning surgery. Refractive outcomes were satisfying with a mean residual refractive astigmatism below 0.50 D.

Trial registration: Registered at Clinicaltrials.gov NCT03803852 ; on May 17, 2022.

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.