International Journal of Retina and Vitreous最新文献

Purpose: To develop a deep learning (DL) model for segmenting retinal hard exudates (HE) from optical coherence tomography (OCT) scans.

Methods: A modified U-Net architecture was trained on manually segmented OCT B-scans of retinal HE. The training set included 1,811 OCT scans from 15 patients with diabetic retinopathy or branch retinal vein occlusion. The model was evaluated using Dice coefficient and accuracy in idependant test set, and its HE area and volume predictions were compared to manually measured HE areas from a previous clinical study. Additionally, a 2D projected image was generated from the 3D structure of the predicted HE.

Results: The DL model achieved a Dice coefficient of 0.721 and an accuracy of 99.9% on the test set. There was a moderate correlation between model-predicted HE volume and area and manually measured HE area from fundus photographs (R = 0.589 and 0.618, respectively; both P < 0.001). The projected 2D image generated from the model accurately visualized HE details, demonstrating better structural information compared to fundus photographs.

Conclusion: The proposed DL model enables accurate segmentation of retinal HE, offering volumetric data with both horizontal and vertical structural information, which enhances visualization and quantification compared to traditional 2D imaging.

Background: The treatment of retinoblastoma (Rb) has undergone significant improvements over the last century. This study aims to assess the trend of enucleation and mortality in retinoblastoma patients during the last 3 decades.

Methods: The study utilized data from the referral center for ocular oncology in Rasool Akram Hospital, Tehran, Iran. It included all patients diagnosed with Rb from August 1991 to December 2018. The study investigated the trend of enucleation and mortality during three-time intervals: before 2001 (T1), during 2001-2007 (T2), and 2008-2018 (T3). Additionally, it assessed the trend of enucleation and mortality based on laterality, age and presentation of Rb (strabismus and leukocoria).

Results: The incidence of enucleation decreased significantly from T1 to T3 (74-41%) during the study period (p-value < 0.001). Pairwise comparisons between T1 and T3 revealed a significant decrease in the incidence of enucleation (74% vs. 41%, p-value < 0.001). The study also demonstrated a significant reduction in the incidence of enucleation when comparing T2 to T3 (60% vs. 41%, p-value < 0.001). Comparing time intervals, there was no significant difference between T2 and T3 regarding the incidence of death (4% vs. 1%), but both intervals had statistically significant lower death rates compared with T1 (26%, both p-values < 0.001).

Conclusion: This study revealed that Introduction of systemic chemotherapy as mainstay of Rb treatment, has led to a significant reduction in mortality and morbidity rates. The incorporation of targeted chemotherapy has further decreased the need for enucleation, but it has not substantially impacted the mortality rate. Unfortunately, in spite of reduction trend in enucleation, systemic and targeted chemotherapy was unable to save the affected globe in nearly half of the patients, even when the malignancy was diagnosed in the earlier stages.

Diabetic macular edema (DME) is one of the leading causes of blindness in diabetic retinopathy. As a domestically developed fusion protein drug in China, the anti-vascular endothelial growth factor (VEGF) agent Conbercept has demonstrated significant efficacy in the treatment of DME in recent years. This systematic review explores the mechanism of action and latest research advancements of Conbercept in DME treatment, integrating clinical trial data and comparisons with other anti-VEGF therapies. It further discusses existing limitations in current research and proposes future research directions.

Background: Age-related macular degeneration (AMD), a leading cause of vision loss in elderly individuals, is a multifactorial disease driven by genetic, environmental, and cellular aging processes. Emerging evidence highlights the critical role of ribonucleic acid (RNA) splicing dysfunction in AMD pathogenesis, with a focus on the U1 small nuclear ribonucleoprotein (U1 snRNP) complex, a key spliceosome component. U1 snRNPs ensure the fidelity of RNA cotranscription and pre-mRNA splicing initiation, and their dysfunction has been implicated in neurodegenerative disorders and other age-related diseases.

Main body: This narrative review explores the impact of U1 snRNP dysregulation on retinal cells, focusing on its role in transcriptomic instability, impaired protein homeostasis, cellular stress, impaired autophagy, and inflammation, which are important features of AMD pathogenesis. Finally, we propose that targeting U1 snRNP dysfunction could provide a novel therapeutic approach to slow, prevent, or restore retinal degeneration, offering insights into broader implications for age-related diseases.

Short conclusion: Understanding the molecular mechanisms underlying U1 snRNP dynamics in retinal health and degeneration is essential for developing innovative and effective treatments for AMD, which may provide ways to delay or reverse the effects of aging and associated diseases.

Background: Preoperative optical coherence tomography (OCT) biomarkers may help predict visual outcomes after idiopathic epiretinal membrane (ERM) surgery. Artificial intelligence (AI) enables automated, quantitative analysis of retinal structure, potentially improving prognostication.

Methods: In this multicenter, retrospective study, patients with idiopathic ERM who underwent pars plana vitrectomy (PPV) were included. Preoperative OCT volume scans were analyzed using an AI-based platform (Discovery OCT Biomarker Detector; RetinAI AG) to quantify retinal layer thicknesses and fluid biomarkers within the central 1 mm Early Treatment Diabetic Retinopathy Study (ETDRS) grid. Extracted features included outer nuclear layer (ONL), combined photoreceptor and retinal pigment epithelium complex (PR + RPE), retinal nerve fiber layer (RNFL) thickness, and intraretinal fluid (IRF) volume. A random forest classifier was used to evaluate the importance of these biomarkers in predicting 12-month best-corrected visual acuity (BCVA), categorizing patients as significant improvers (≥ 0.2 logMAR gain) or minimal/non-responders.

Results: A total of 71 eyes were analyzed. Mean BCVA improved from 0.51 ± 0.41 to 0.25 ± 0.33 logMAR at 12 months postoperatively (P < 0.001). Thinner preoperative ONL thickness was strongly associated with worse final BCVA (r = - 0.54), while thicker RNFL (r = 0.28) and greater IRF volume (r = - 0.26) were also linked to poorer outcomes. The random forest model achieved an area under the curve (AUC) of 0.71 for predicting visual improvement, identifying PR + RPE thickness, RNFL thickness, and ONL thickness as the most influential predictors.

Conclusions: Preoperative AI-derived OCT biomarkers, particularly indicators of outer retinal thinning and inner retinal thickening, are associated with limited visual recovery following ERM surgery. Integration of automated biomarker analysis into preoperative assessment may help identify patients at higher risk of suboptimal postoperative vision, informing surgical decision-making and patient counseling.