Intraocular pressure modifications in patients with acute central/hemicentral retinal vein occlusions

Anatomical, Functional, and Prognostic Results of Vitrectomy in Epiretinal Membranes Secondary to Retinal Vein Occlusions

INTRODUCTION

We investigated the anatomical and functional results of vitrectomy associated with the peeling of secondary epiretinal membranes (ERM) in patients with retinal vein occlusion (RVO) and determined the prognostic factors of surgical outcomes.

METHODS

This retrospective, multicenter, observational study included 50 patients with RVO who underwent vitrectomy with ERM removal between July 2012 and February 2021. Visual acuity (VA) and central macular thickness (CMT) were investigated up to 3 years. Univariate analysis identified the predictive factors associated with functional and anatomical outcomes.

RESULTS

Fifty eyes from 50 patients (62% with central RVO) were included. The mean VA of 0.9 ± 0.7 logMAR preoperatively improved to 0.5 ± 0.5 logMAR after 24 months (p = 0.01). Anatomically, the mean preoperative CMT was 501 ± 168 μm, decreasing to 348 ± 108 μm at month 24 (p = 0.008). By 36 months, VA had improved or stabilized in 90% of the eyes, whereas CMT had been reduced by at least 20% from baseline in 80% of the eyes. A lower number of intravitreal injections (IVI) were required after vitrectomy. Worse preoperative VA, absence of preoperative panretinal photocoagulation, and postoperative use of adjunctive IVI were associated with VA recovery. Higher baseline CMT and the use of preoperative dexamethasone injections were associated with an improvement in CMT.

CONCLUSION

Vitrectomy for ERM secondary to RVO was effective in improving VA and recovering CMT for up to 3 years and reduced the number of IVIs.

INTRODUCTION

We investigated the anatomical and functional results of vitrectomy associated with the peeling of secondary epiretinal membranes (ERM) in patients with retinal vein occlusion (RVO) and determined the prognostic factors of surgical outcomes.

METHODS

This retrospective, multicenter, observational study included 50 patients with RVO who underwent vitrectomy with ERM removal between July 2012 and February 2021. Visual acuity (VA) and central macular thickness (CMT) were investigated up to 3 years. Univariate analysis identified the predictive factors associated with functional and anatomical outcomes.

RESULTS

Fifty eyes from 50 patients (62% with central RVO) were included. The mean VA of 0.9 ± 0.7 logMAR preoperatively improved to 0.5 ± 0.5 logMAR after 24 months (p = 0.01). Anatomically, the mean preoperative CMT was 501 ± 168 μm, decreasing to 348 ± 108 μm at month 24 (p = 0.008). By 36 months, VA had improved or stabilized in 90% of the eyes, whereas CMT had been reduced by at least 20% from baseline in 80% of the eyes. A lower number of intravitreal injections (IVI) were required after vitrectomy. Worse preoperative VA, absence of preoperative panretinal photocoagulation, and postoperative use of adjunctive IVI were associated with VA recovery. Higher baseline CMT and the use of preoperative dexamethasone injections were associated with an improvement in CMT.

CONCLUSION

Vitrectomy for ERM secondary to RVO was effective in improving VA and recovering CMT for up to 3 years and reduced the number of IVIs.

Automatic Diagnosis of Different Types of Retinal Vein Occlusion Based on Fundus Images

Retinal vein occlusion (RVO) is the second common cause of blindness following diabetic retinopathy. The manual screening of fundus images to detect RVO is time consuming. Deep‐learning techniques have been used for screening RVO due to their outstanding performance in many applications. However, unlike other images, medical images have smaller lesions, which require a more elaborate approach. To provide patients with an accurate diagnosis, followed by timely and effective treatment, we developed an intelligent method for automatic RVO screening on fundus images. Swin Transformer learns the hierarchy of low‐to high‐level features like the convolutional neural network. However, Swin Transformer extracts features from fundus images through attention modules, which pay more attention to the interrelationship between the features and each other. The model is more universal, does not rely entirely on the data itself, and focuses not only on local information but has a diffusion mechanism from local to global. To suppress overfitting, we adopt a regularization strategy, label smoothing, which uses one‐hot to add noise to reduce the weight of the categories of true sample labels when calculating the loss function. The choice of different models using a 5‐fold cross‐validation on our own datasets indicates that Swin Transformer performs better. The accuracy of classifying all datasets is 98.75 ± 0.000, and the accuracy of identifying MRVO, CRVO, BRVO, and normal, using the method proposed in the paper, is 94.49 ± 0.094, 99.98 ± 0.015, 98.88 ± 0.08, and 99.42 ± 0.012, respectively. The method will be useful to diagnose RVO and help decide grade through fundus images, which has the potency to provide patients with further diagnosis and treatment.