Optical coherence tomography imaging of macular oedema

Aflibercept or ranibizumab for diabetic macular edema

Background

Vascular endothelial growth factor (VEGF) is the primary substance involved in retinal barrier breach. VEGF overexpression may cause diabetic macular edema (DME). Laser photocoagulation of the macula is the standard treatment for DME; however, recently, intravitreal anti-VEGF injections have surpassed laser treatment. Our aim was to evaluate the efficacy of intravitreal injections of aflibercept or ranibizumab for managing treatment-naive DME.

Methods

This single-center, retrospective, interventional, comparative study included eyes with visual impairment due to treatment-naive DME that underwent intravitreal injection of either aflibercept 2 mg/0.05 mL or ranibizumab 0.5 mg/0.05 mL at Al-Azhar University Hospitals, Egypt between March 2023 and January 2024. Demographic data and full ophthalmological examination results at baseline and 1, 3, and 6 months post-injection were collected, including the best-corrected distance visual acuity (BCDVA) in logarithm of the minimum angle of resolution (logMAR) notation, slit-lamp biomicroscopy, dilated fundoscopy, and central subfield thickness (CST) measured using spectral-domain optical coherence tomography.

Results

Overall, the 96 eyes of 96 patients with a median (interquartile range [IQR]) age of 57 (10) (range: 20-74) years and a male-to-female ratio of 1:2.7 were allocated to one of two groups with comparable age, sex, diabetes mellitus duration, and presence of other comorbidities (all P >0.05). There was no statistically significant difference in baseline diabetic retinopathy status or DME type between groups (both P >0.05). In both groups, the median (IQR) BCDVA significantly improved from 0.7 (0.8) logMAR at baseline to 0.4 (0.1) logMAR at 6 months post-injection (both P = 0.001), with no statistically significant difference between groups at all follow-up visits (all P >0.05). The median (IQR) CST significantly decreased in the aflibercept group from 347 (166) µm at baseline to 180 (233) µm at 6 months post-injection, and it decreased in the ranibizumab group from 360 (180) µm at baseline to 190 (224) µm at 6 months post-injection (both P = 0.001), with no statistically significant differences between groups at all follow-up visits (all P >0.05). No serious adverse effects were documented in either group.

Conclusions

Ranibizumab and aflibercept were equally effective in achieving the desired anatomical and functional results in patients with treatment-naïve DME in short-term follow-up without significant differences in injection counts between both drugs. Larger prospective, randomized, double-blinded trials with longer follow-up periods are needed to confirm our preliminary results.

Risk Factors Associated with Cystoid Macular Edema among Patients Undergoing Primary Repair of Rhegmatogenous Retinal Detachment

PURPOSE

To investigate predictors of the development and resolution of cystoid macular edema (CME) after rhegmatogenous retinal detachment (RRD) repair.

DESIGN

Retrospective cross sectional study.

SUBJECTS

Patients who underwent primary repair of uncomplicated RRD.

METHODS

Demographics, ophthalmic history, visual acuity, RRD features, time to development/resolution of CME, OCT characteristics of CME/epiretinal membrane (ERM), type of surgery, and treatments were collected. Logistic regressions were used to identify predictors of CME development and resolution.

MAIN OUTCOME MEASURES

Predictors of CME development and resolution.

RESULTS

A total of 708 eyes were included, of which 55 (7.8%) developed CME. Factors associated with an increased risk of CME development included total number of retinal detachment surgeries (odds ratio [OR] 1.66 [1.24-2.23], P < 0.001), prior intraocular surgery (OR 4.43 [1.19-16.51], P = 0.03), and presence of ERM after surgery (OR 4.49 [2.30-8.74], P < 0.001). Patients undergoing pars plana vitrectomy (PPV) were more likely to develop CME compared with patients undergoing scleral buckling (SB; OR 3.09 [1.18-8.10], P = 0.02). A longer average time to CME detection was associated with lower CME resolution (OR 0.94 [0.89-0.998], P = 0.04). In patients who developed an ERM postsurgically, those who developed CME after ERM had a lower rate of resolution compared with those who developed CME before ERM (P = 0.03).

CONCLUSIONS

Cystoid macular edema may be more likely to develop in patients undergoing PPV than SB, those who underwent more surgeries for RRD repair, those who had prior intraocular surgery, or those who developed an ERM after RRD repair. Resolution of CME may be affected by the time to detection of CME and ERM development.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Prevalence and Distribution of Macular Fluid with Central Retinal Artery Occlusion and Anterior Ischemic Optic Neuropathy

Purpose

To examine the prevalence and distribution of fluid within a cohort of eyes with acute central retinal artery occlusion (CRAO) and non-arteritic anterior ischemic optic neuropathy (AION) using optical coherence tomography (OCT).

Methods

A retrospective analysis of patient records and OCT imaging. Patients presenting with acute CRAO or AION who had available macular OCT imaging and no co-morbidities known to cause macular fluid were included in the analysis. Baseline characteristics, visual acuity (VA), and fluid presence and distribution among the retinal layers were recorded.

Results

In the 16 eyes with acute CRAO, fluid was noted in 5 eyes (31%), which was mainly subretinal (3 eyes) or intraretinal located within the outer retinal layers (3 eyes). Only one eye had inner retinal cysts. Of the 11 eyes with acute AION, fluid was present in 8 eyes (73%). Subretinal fluid was noted in 4 eyes and extended to the foveal area in 3 of them, and outer retinal versus inner retinal cysts were noted in 6 versus 3 eyes, respectively. None of the eyes showed hard exudate deposition. In the small subset of eyes with CRAO and macular fluid that were followed-up, VA improved, while in eyes with AION, VA remained stable.

Conclusion

Macular fluid on OCT is not an uncommon feature of acute CRAO and AION and is mainly distributed within the outer retinal layers or subretinal space. Fluid is an understudied feature of retinal and optic nerve head infarction and may have a role in predicting neuronal damage extent and visual outcome.

Cystoid macular edema as a complication of central retinal artery occlusion

Purpose

To describe the development of cystoid macular edema (CME) as a complication of central retinal artery occlusion (CRAO) in 2 cases.

Observations

The first patient was a 51-year-old female who presented with acute loss of vision in the left eye. Multimodal retinal imaging revealed a CRAO with a perfused cilioretinal artery. CME acutely developed one week after presentation. Cystoid spaces predominantly involved the outer nuclear layer (ONL) on optical coherence tomography (OCT) and completely resolved in two weeks. The second case was a 50-year-old man who presented with acute vision loss in the right eye for 3 weeks. Multimodal retinal imaging illustrated an acute CRAO of the right eye. Four weeks later, visual acuity spontaneously improved to 20/20 and was maintained at 20/20 for more than 2 years. After 28 months, the patient returned with a recurrent drop of vision in the right eye. Cross sectional and en face OCT revealed CME in the right eye without leakage on FA. Cystoid spaces predominantly involved the inner nuclear layer (INL) and resolved with intravitreal anti-VEGF injection combined with carbonic anhydrase inhibitor (CAI) and steroid topical drop therapy.

Conclusions and Importance

CME can rarely complicate both the acute and chronic phase of CRAO. In the acute phase, cystoid spaces were transient and confined to the ONL on OCT. While in the chronic phase, cystoid spaces were confined to the INL on OCT and angiographically silent on FA. Further studies are needed to identify the incidence, underlying pathophysiology and visual prognosis of CME in cases of CRAO.

Correlation of Aqueous, Vitreous, and Serum Protein Levels in Patients With Retinal Diseases

Purpose

To further establish aqueous humor (AH) as a clinically suitable source of protein biomarkers in retinal diseases by evaluating the correlation of a large panel of proteins between AH, vitreous humor (VH), and serum (SE).

Methods

We enrolled 60 subjects (eyes) with various non-infectious retinal diseases. AH, VH, and SE proteins were analyzed using the Olink Target 96 platform (1196 protein assays in total). We compared these three matrices in terms of quantification overlap, principal component analysis, and correlation.

Results

In the AH, VH, and SE samples, 841, 917, and 1133 proteins, respectively, were consistently quantified above the limit of detection in more than 30% of patients. AH and VH shared 812 of these proteins. AH and VH samples overlapped along principal component 1, but SE samples were distinct. We identified 490 proteins with significant (false discovery rate [FDR]-adjusted P < 0.05) and relevant correlations (correlation coefficient > 0.5) between AH and VH, compared to only 33 and 40 proteins for VH and SE and for AH and SE, respectively.

Conclusions

Due to a close correlation between protein concentrations in the AH and VH and a clear difference from the SE, AH has the potential to serve as a substitute for VH and may hold significance in identifying protein biomarkers and novel targets related to retinal diseases.

Translational Relevance

This study further supports AH as a clinically suitable source of protein biomarkers in retinal diseases. In addition, the identified AH and VH correlations can inform the selection of protein biomarker candidates in future translational research.

Prevalence of bacillary layer detachment in diabetic macular edema and response to 3 anti-vascular endothelial growth factor treatment

Spectral-domain optical coherence tomography is widely used in maculopathy, including diabetic macular edema (DME). Bacillary layer detachment (BALAD) is a novel optical coherence tomography finding, defined as the separation of the intraretinal layer between the inner segment myoids and ellipsoids. A total of 161 treatment-naïve eyes with centrally involved DME that underwent 3 monthly loading doses of anti-vascular endothelial growth factor (anti-VEGF) intravitreal injections were enrolled and analyzed retrospectively. BALAD was found in 6.2% of eyes with concurrent subretinal fluid (SRF). All eyes were divided into 3 groups: no either group had neither SRF or BALAD; the SRF only group had SRF but no BALAD; and the BALAD group had both SRF and BALAD. A significant increase in baseline central foveal thickness (CFT) in the BALAD group was observed (no either vs SRF only vs BALAD, baseline CFT: 387.6 ± 74.29 vs 440.6 ± 106.79 vs 642.0 ± 188.86; P < .01). Total resolution of BALAD was noted after anti-VEGF therapy, along with a significant decrease in CFT in all groups (CFT decrease: 82.4 ± 87.07 vs 187.6 ± 138.88 vs 252.1 ± 127.63; P < .01). Eyes with BALAD tended to have the worst baseline visual acuity (baseline logarithm of the minimum angle of resolution VA: 0.76 ± 0.353 vs 0.63 ± 0.303 vs 1.15 ± 0.300; P = .046) but showed the most improvement after treatment (logarithm of the minimum angle of resolution VA change: -0.14 ± 0.235 vs -0.22 ± 0.275 vs -0.27 ± 0.250; P = .079). After resolution of BALAD, all eyes in the BALAD group exhibited ellipsoid zone and/or interdigitation zone disruption corresponding to the BALAD area. BALAD is a novel optical coherence tomography finding associated with a spectrum of diseases including DME. With anti-VEGF therapy, total resolution of BALAD and a significant decrease in CFT can be obtained. However, ellipsoid zone/interdigitation zone disruption tended to develop.

Severity of cystoid macular oedema in preterm infants observed using hand-held spectral domain optical coherence tomography improves weekly with postmenstrual age

OBJECTIVE

To investigate the relationship between cystoid macular oedema (CMO) measured in preterm infants using hand-held spectral domain optical coherence tomography (HH SD-OCT), with gestational age at birth (GA), birthweight (BW), diagnosis of retinopathy of prematurity (ROP) and the presence or absence of the external limiting membrane (ELM).

METHODS

We conducted a prospective mixed cross-sectional/longitudinal observational study of 112 participants (23 to 36 weeks GA; n = 25 with, and n = 87 without, CMO). Retinal images were acquired using 344 HH SD-OCT (n = 66 with and n = 278 without, CMO) between 31 to 44 weeks postmenstrual age (PMA). CMO type ('fovea' and 'dome') was measured using thickness, width, area and peak.

RESULTS

CMO was observed in 22.9% of preterm infants, and 19.2% of images. The mean values for  thickness, width, area and peak of 'dome' CMO were 128.47 µm (SD +/- 34.23), 3624.45 µm (SD +/- 1323.03), 0.49 mm2 (SD +/- 0.28) and 279.81 µm (SD +/- 13.57) respectively. The mean values for  thickness, width, area and peak of 'fovea' CMO were 64.37 µm (SD +/- 17.11), 2226.28 µm (SD +/- 1123.82), 0.16 mm2 (SD +/- 0.11) and 95.03 µm (SD +/- 26.99) respectively. Thickness, area width and peak were significantly greater for 'dome CMO compared with 'fovea' CMO (P < 0.0001 for thickness, area and peak; P < 0.01 for width). Area and width significantly decreased with PMA for 'dome' and 'fovea' CMO (p = 0.0028; p < 0.001 respectively). No association was found between the presence of ROP and the detection of CMO or detection of CMO with absence of ELM.

CONCLUSIONS

HH -OCT in preterm infants demonstrates that the severity of CMO appearance improves each week for both fovea and dome CMO.

Few-shot out-of-distribution detection for automated screening in retinal OCT images using deep learning

Deep neural networks have been increasingly proposed for automated screening and diagnosis of retinal diseases from optical coherence tomography (OCT), but often provide high-confidence predictions on out-of-distribution (OOD) cases, compromising their clinical usage. With this in mind, we performed an in-depth comparative analysis of the state-of-the-art uncertainty estimation methods for OOD detection in retinal OCT imaging. The analysis was performed within the use-case of automated screening and staging of age-related macular degeneration (AMD), one of the leading causes of blindness worldwide, where we achieved a macro-average area under the curve (AUC) of 0.981 for AMD classification. We focus on a few-shot Outlier Exposure (OE) method and the detection of near-OOD cases that share pathomorphological characteristics with the inlier AMD classes. Scoring the OOD case based on the Cosine distance in the feature space from the penultimate network layer proved to be a robust approach for OOD detection, especially in combination with the OE. Using Cosine distance and only 8 outliers exposed per class, we were able to improve the near-OOD detection performance of the OE with Reject Bucket method by [Formula: see text] 10% compared to without OE, reaching an AUC of 0.937. The Cosine distance served as a robust metric for OOD detection of both known and unknown classes and should thus be considered as an alternative to the reject bucket class probability in OE approaches, especially in the few-shot scenario. The inclusion of these methodologies did not come at the expense of classification performance, and can substantially improve the reliability and trustworthiness of the resulting deep learning-based diagnostic systems in the context of retinal OCT.

Methods of ex vivo analysis of tissue status in vascularized composite allografts

Vascularized composite allotransplantation can improve quality of life and restore functionality. However, the complex tissue composition of vascularized composite allografts (VCAs) presents unique clinical challenges that increase the likelihood of transplant rejection. Under prolonged static cold storage, highly damage-susceptible tissues such as muscle and nerve undergo irreversible degradation that may render allografts non-functional. Skin-containing VCA elicits an immunogenic response that increases the risk of recipient allograft rejection. The development of quantitative metrics to evaluate VCAs prior to and following transplantation are key to mitigating allograft rejection. Correspondingly, a broad range of bioanalytical methods have emerged to assess the progression of VCA rejection and characterize transplantation outcomes. To consolidate the current range of relevant technologies and expand on potential for development, methods to evaluate ex vivo VCA status are herein reviewed and comparatively assessed. The use of implantable physiological status monitoring biochips, non-invasive bioimpedance monitoring to assess edema, and deep learning algorithms to fuse disparate inputs to stratify VCAs are identified.

Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review

The aim of this systematic literature review is twofold, (1) detail the impact of retinal biomarkers identifiable via optical coherence tomography (OCT) on disease progression and response to treatment in neovascular age-related macular degeneration (nAMD) and (2) establish which biomarkers are currently identifiable by artificial intelligence (AI) models and the utilisation of this technology. Following the PRISMA guidelines, PubMed was searched for peer-reviewed publications dated between January 2016 and January 2022.

POPULATION

Patients diagnosed with nAMD with OCT imaging.

SETTINGS

Comparable settings to NHS hospitals.

STUDY DESIGNS

Randomised controlled trials, prospective/retrospective cohort studies and review articles. From 228 articles, 130 were full-text reviewed, 50 were removed for falling outside the scope of this review with 10 added from the author's inventory, resulting in the inclusion of 90 articles. From 9 biomarkers identified; intraretinal fluid (IRF), subretinal fluid, pigment epithelial detachment, subretinal hyperreflective material (SHRM), retinal pigmental epithelial (RPE) atrophy, drusen, outer retinal tabulation (ORT), hyperreflective foci (HF) and retinal thickness, 5 are considered pertinent to nAMD disease progression; IRF, SHRM, drusen, ORT and HF. A number of these biomarkers can be classified using current AI models. Significant retinal biomarkers pertinent to disease activity and progression in nAMD are identifiable via OCT; IRF being the most important in terms of the significant impact on visual outcome. Incorporating AI into ophthalmology practice is a promising advancement towards automated and reproducible analyses of OCT data with the ability to diagnose disease and predict future disease conversion.

SYSTEMATIC REVIEW REGISTRATION

This review has been registered with PROSPERO (registration ID: CRD42021233200).

Optical Coherence Tomography: Focus on the Pathology of Macula in Scleritis Patients

Optical coherence tomography (OCT) is a non-invasive imaging technique for high-resolution, cross-sectional tissue imaging of the eye. During the past two and a half decades, OCT has become an essential tool in ophthalmology. It is a painless method for examining details of ocular structures in vivo with high resolution that has revolutionized patient care following and treating scleritis patients.

METHODS

Twenty-four patients diagnosed with scleritis were selected for this study. All of the patients went through basic ophthalmological examinations, such as visual acuity testing (VA), intraocular pressure measurement (IOP), slit lamp examination, ophthalmoscopic examination, and OCT. OCT examinations were taken by SD-OCT Spectralis OCT system (Heidelberg Engineering, Heidelberg, Germany).

RESULTS

Twenty-seven eyes of 24 patients (7 males and 17 females) were included in this study, who were diagnosed with scleritis. OCT examinations showed epiretinal membrane (ERM) in three patients (12%), cystoid macular edema (CME) (three cases, 12%), diffuse macular edema (DME) (one case, 4%), and serous retinal detachment (SRD) (one case, 4%).

CONCLUSIONS

OCT proved to be a valuable, non-invasive method for detecting macular pathology in patients with scleritis. Despite the best treatment regimen applied, macular involvement resulting in reduced visual acuity (VA) can develop, which we could detect with OCT since macular edema (ME) is the leading cause of decreased vision due to the damaged outer blood-retina barrier (BRB) in inflammation. OCT investigation is a highly important method for early detection of ocular complications in scleritis in order to prevent blindness.

Self-supervised anomaly detection, staging and segmentation for retinal images

Unsupervised anomaly detection (UAD) is to detect anomalies through learning the distribution of normal data without labels and therefore has a wide application in medical images by alleviating the burden of collecting annotated medical data. Current UAD methods mostly learn the normal data by the reconstruction of the original input, but often lack the consideration of any prior information that has semantic meanings. In this paper, we first propose a universal unsupervised anomaly detection framework SSL-AnoVAE, which utilizes a self-supervised learning (SSL) module for providing more fine-grained semantics depending on the to-be detected anomalies in the retinal images. We also explore the relationship between the data transformation adopted in the SSL module and the quality of anomaly detection for retinal images. Moreover, to take full advantage of the proposed SSL-AnoVAE and apply towards clinical usages for computer-aided diagnosis of retinal-related diseases, we further propose to stage and segment the anomalies in retinal images detected by SSL-AnoVAE in an unsupervised manner. Experimental results demonstrate the effectiveness of our proposed method for unsupervised anomaly detection, staging and segmentation on both retinal optical coherence tomography images and color fundus photograph images.

Measurement of Foveal Retinal Thickness in Myopic Patients Using Different Display Modes on Optical Coherence Tomography: A Retrospective, Cross-Sectional Study

INTRODUCTION

The aim of this work is to investigate the differences in the measurement of foveal retinal thickness in myopic patients between two display modes (1:1 pixel and 1:1 micron) on optical coherence tomography (OCT).

METHODS

Horizontal OCT line scan through the central fovea was used for manual measurement of foveal retinal thickness under the two display modes, and the values were compared using Wilcoxon signed-rank test. Correlations between the OCT image tilting angle (OCT ITA) and differences in OCT measurement were analyzed by Spearman's test.

RESULTS

127 participants with a median age of 28 years, a median spherical equivalent (SE) of - 8.5 D, and a median axial length (AL) of 27.04 mm. There were significant differences between the two display modes, with a median absolute difference (median relative difference) of 13.33 μm (2.75%) for the central foveal thickness (CFT), 5.33 μm (1.28%) for the Henle fiber and outer nuclear layer thickness (HFL + ONL), 3 μm (6.47%) for the external limiting membrane to ellipsoid zone distance (ELM-EZ), and 4 μm (8.77%) for the ellipsoid zone to retinal pigment epithelium distance (EZ-RPE) (all p < 0.05). The differences in foveal retinal thickness between the two display modes were significantly correlated with the OCT ITA (r = 0.732 for CFT, 0.561 for HFL + ONL, 0.642 for ELM-EZ, and 0.471 for EZ-RPE, all p < 0.05).

CONCLUSIONS

Disparities between the two display modes were found in the manual measurement of foveal retinal thickness and correlated to the OCT ITA.

Classification of Retinal Diseases in Optical Coherence Tomography Images Using Artificial Intelligence and Firefly Algorithm

In recent years, the number of studies for the automatic diagnosis of biomedical diseases has increased. Many of these studies have used Deep Learning, which gives extremely good results but requires a vast amount of data and computing load. If the processor is of insufficient quality, this takes time and places an excessive load on the processor. On the other hand, Machine Learning is faster than Deep Learning and does not have a much-needed computing load, but it does not provide as high an accuracy value as Deep Learning. Therefore, our goal is to develop a hybrid system that provides a high accuracy value, while requiring a smaller computing load and less time to diagnose biomedical diseases such as the retinal diseases we chose for this study. For this purpose, first, retinal layer extraction was conducted through image preprocessing. Then, traditional feature extractors were combined with pre-trained Deep Learning feature extractors. To select the best features, we used the Firefly algorithm. In the end, multiple binary classifications were conducted instead of multiclass classification with Machine Learning classifiers. Two public datasets were used in this study. The first dataset had a mean accuracy of 0.957, and the second dataset had a mean accuracy of 0.954.

Mechanisms of macular edema

Macular edema is the pathological accumulation of fluid in the central retina. It is a complication of many retinal diseases, including diabetic retinopathy, retinal vascular occlusions and uveitis, among others. Macular edema causes decreased visual acuity and, when chronic or refractory, can cause severe and permanent visual impairment and blindness. In most instances, it develops due to dysregulation of the blood-retinal barrier which permits infiltration of the retinal tissue by proteins and other solutes that are normally retained in the blood. The increase in osmotic pressure in the tissue drives fluid accumulation. Current treatments include vascular endothelial growth factor blockers, corticosteroids, and non-steroidal anti-inflammatory drugs. These treatments target vasoactive and inflammatory mediators that cause disruption to the blood-retinal barrier. In this review, a clinical overview of macular edema is provided, mechanisms of disease are discussed, highlighting processes targeted by current treatments, and areas of opportunity for future research are identified.

Non-steroidal anti-inflammatory agents for treating cystoid macular edema following cataract surgery

BACKGROUND

Cataract surgery is the most common ambulatory incisional surgery performed in the USA. Cystoid macular edema (CME), the accumulation of fluid in the central retina due to leakage from dilated capillaries, is the most common cause of vision impairment following cataract surgery. Acute CME, defined as CME of less than four months' duration, often resolves spontaneously. CME that persists for four months or longer is termed chronic CME. Non-steroidal anti-inflammatory drugs (NSAIDs) have been used to treat CME. This update adds new evidence and analyses to the previously published review.

OBJECTIVES

To examine the effectiveness of NSAIDs in the treatment of CME following cataract surgery.

SEARCH METHODS

We searched the CENTRAL (2022, Issue 3); Ovid MEDLINE; Embase; PubMed; LILACS; mRCT (discontinued in 2014, last searched August 2011), ClinicalTrials.gov, and WHO ICTRP databases. We did not use any date or language restrictions in the electronic search for trials. We last searched the electronic databases on 20 March 2022.   SELECTION CRITERIA: We included randomized controlled trials evaluating the effects of NSAIDs for CME following cataract surgery.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened all titles and abstracts, reviewed full-text publications against eligibility criteria, independently extracted data from newly included trials and assessed risk of bias for each included trial. We contacted trial authors for clarification or to request missing information. We provided a narrative synthesis of all included trials and their results. For continuous and dichotomous outcomes, we separately performed pooled analysis and reported mean difference (MD) and risk ratio (RR) as well as the associated 95% confidence interval (CI) whenever feasible. Two review authors independently graded the overall certainty of the evidence for each outcome using the GRADE approach.

MAIN RESULTS

We included nine trials with a total of 390 participants (393 eyes). Study participants' mean age was 72.2 years (interquartile range [IQR] 68.8 to 73.6) and 72% were women (IQR 69% to 74%). Three trials included participants with acute CME, and four included participants with chronic CME; the remaining two trials enrolled both participants with acute and chronic CME or participants with unknown CME duration. We assessed trials as having unclear (33%) or high risk of bias (67%). Visual improvement of two or more lines at the end of treatment  Data from one trial in participants with acute CME show no treatment effect of topical ketorolac compared to placebo (RR 2.00, 95% CI 0.46 to 8.76; 22 participants). Data from a three-arm trial in participants with acute CME demonstrate that, when compared with topical prednisolone, topical ketorolac (RR 1.33, 95% CI 0.58 to 3.07; 17 participants) or topical ketorolac and prednisolone combination therapy (RR 1.78, 95% CI 0.86 to 3.69; 17 participants) may have little or no effect on visual improvement. Results of subgroup analysis from two studies in participants with chronic CME suggest that, after treatment for 90 days or longer, NSAIDs may increase participants' likelihood of visual improvement by 1.87 fold (RR 2.87, 95% CI 1.58 to 5.22; I2 = 33%; 2 trials, 121 participants) relative to placebo. However, there was no evidence of treatment effects in the subgroup with two months of treatment or less (RR 0.72, 95% CI 0.30 to 1.73; P = 0.19, I2 = 41%; 2 trials, 34 participants). Overall, this evidence is very low certainty.  A single-study estimate in patients with mixed CME indicates that topical diclofenac may increase the likelihood of visual improvement by 40% when compared to topical ketorolac (RR 1.40, 95% CI 1.02 to 1.94; 68 participants). However, the same trial reported no difference between the groups in mean final visual acuity in Snellen lines (MD 0.40, 95% CI -0.93 to 1.73). A three-arm trial in patients with mixed CME reporting visual changes in ETDRS letters in comparisons between ketorolac and diclofenac (34 participants) or bromfenac (34 participants) suggests no evidence of effects. Overall, NSAIDs may slightly improve visual acuity in participants with mixed CME but the evidence is very uncertain. Persistence of improvement of vision one month after discontinuation of treatment One trial of participants with chronic CME tested oral indomethacin (RR 0.40, 95% CI 0.10 to 1.60; 20 participants) and the other compared topical ketorolac to placebo (RR 4.00, 95% CI 0.51 to 31.1; 26 participants). While there is no evidence of treatment effects, evidence suggests substantial between-group heterogeneity (P = 0.07, I2 = 69.9%; very low-certainty evidence). None of the trials in patients with acute or mixed CME reported this outcome. Proportion of participants with improvement in leakage on fundus fluorescein angiography One three-arm trial in participants with acute CME shows that, when compared with topical prednisolone, there is no treatment benefit of topical ketorolac (RR 1.11, 95% CI 0.45 to 2.75; 17 participants) or topical ketorolac and topical prednisolone combination therapy (RR 1.56, 95% CI 0.72 to 3.38; 17 participants). This evidence is very low certainty. The combined estimate from two trials in participants with chronic CME indicates NSAIDs have little to no effect over placebo on improving leakage (RR 1.93, 95% CI 0.62 to 6.02; 40 participants; very low-certainty evidence). Neither of the trials in patients with mixed CME reported this outcome. Proportion of participants with improved contrast sensitivity Very low-certainty evidence from one trial in participants with acute CME shows no treatment benefit of ketorolac (RR 1.11, 95% CI 0.45 to 2.75; 17 participants) or ketorolac and prednisolone combination therapy (RR 1.78, 95% CI 0.86 to 3.69; 17 participants) compared with topical prednisolone. None of the trials in patients with chronic or mixed CME reported this outcome. Proportion of participants with improved central macular thickness on optical coherence tomography; measures of quality of life No included trial reported these outcomes. Adverse effects Most trials observed no differences in ocular adverse events, such as corneal toxicity or elevated intraocular pressure, between comparison groups.

AUTHORS' CONCLUSIONS

Evidence on effects of NSAIDs in patients with CME is very uncertain and further investigation is warranted. Our findings are limited by small sample sizes, and heterogeneity in interventions, assessments, and reporting of clinically important outcomes.

Structure-Guided Cross-Attention Network for Cross-Domain OCT Fluid Segmentation

Accurate retinal fluid segmentation on Optical Coherence Tomography (OCT) images plays an important role in diagnosing and treating various eye diseases. The art deep models have shown promising performance on OCT image segmentation given pixel-wise annotated training data. However, the learned model will achieve poor performance on OCT images that are obtained from different devices (domains) due to the domain shift issue. This problem largely limits the real-world application of OCT image segmentation since the types of devices usually are different in each hospital. In this paper, we study the task of cross-domain OCT fluid segmentation, where we are given a labeled dataset of the source device (domain) and an unlabeled dataset of the target device (domain). The goal is to learn a model that can perform well on the target domain. To solve this problem, in this paper, we propose a novel Structure-guided Cross-Attention Network (SCAN), which leverages the retinal layer structure to facilitate domain alignment. Our SCAN is inspired by the fact that the retinal layer structure is robust to domains and can reflect regions that are important to fluid segmentation. In light of this, we build our SCAN in a multi-task manner by jointly learning the retinal structure prediction and fluid segmentation. To exploit the mutual benefit between layer structure and fluid segmentation, we further introduce a cross-attention module to measure the correlation between the layer-specific feature and the fluid-specific feature encouraging the model to concentrate on highly relative regions during domain alignment. Moreover, an adaptation difficulty map is evaluated based on the retinal structure predictions from different domains, which enforces the model focus on hard regions during structure-aware adversarial learning. Extensive experiments on the three domains of the RETOUCH dataset demonstrate the effectiveness of the proposed method and show that our approach produces state-of-the-art performance on cross-domain OCT fluid segmentation.

Effect of fingolimod therapy on quantitative macular changes among patients with relapsing-remitting multiple sclerosis: a four-year follow-up study from Oman

PURPOSE

Fingolimod (FTY-720) is an immunomodulatory oral agent approved for the treatment of relapsing-remitting multiple sclerosis (RRMS); however, several clinical trials have shown that some recipients may develop macular oedema (ME) as an adverse reaction. As there are no studies assessing the long-term (> 1 year) effect of fingolimod on the macula, this study aimed to evaluate the quantitative effect of fingolimod therapy on central macular thickness (CMT) and total macular volume (TMV) over a four-year period.

METHODS

This retrospective longitudinal cohort study was performed between January 2014 and December 2018. A total of 21 patients with RRMS receiving fingolimod therapy were recruited and followed-up over 4 years to assess CMT and TMV changes measured using spectral domain optical coherence tomography. A paired sample t-test was used to compare mean CMT and TMV values calculated at baseline prior to the initiation of fingolimod therapy with those observed at three, six, 12, 24, 36 and 48 months of treatment.

RESULTS

None of the patients developed ME over the four-year study period. In addition, there was no significant difference in baseline mean CMT values and those observed at a four-year follow-up. Although mean TMV values remained constant initially, there was a significant decrease towards the end of the study period.

CONCLUSIONS

Long-term fingolimod therapy did not result in significant CFT changes. While there was a reduction in TMV towards the end of the study, this is likely due to the degenerative effect of the disease itself on the nerve fibres of the retina.

Intermediate Uveitis: A Review

PURPOSE

This review aims to provide an update on the clinical presentation, etiologies, complications, and treatment options in intermediate uveitis (IU).

METHODS

Narrative literature review.

RESULTS

IU affects all age groups with no clear gender predominance and has varied etiologies including systemic illnesses and infectious diseases, or pars planitis. In some instances, IU may be the sole presentation of an underlying associated condition or disease. Management of IU and its complications include administration of corticosteroids, antimetabolites, T-cell inhibitors, and/or biologics, along with surgical interventions, with varying degrees of effectiveness across literature. In particular, increasing evidence of the safety and efficacy of immunomodulatory agents and biologics has seen greater adoption of these therapies in clinical practice.

CONCLUSIONS

IU is an anatomical description of uveitis, involving intraocular inflammation of the vitreous, peripheral retinal vasculature, and pars plana. Various treatment options for intermediate uveitis are currently used in practice.

Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.

Universal photonics tomography

3D imaging is essential for the study and analysis of a wide variety of structures in numerous applications. Coherent photonic systems such as optical coherence tomography (OCT) and light detection and ranging (LiDAR) are state-of-the-art approaches, and their current implementation can operate in regimes that range from under a few millimeters to over more than a kilometer. We introduce a general method, which we call universal photonics tomography (UPT), for analyzing coherent tomography systems, in which conventional methods such as OCT and LiDAR may be viewed as special cases. We demonstrate a novel approach (to our knowledge) based on the use of phase modulation combined with multirate signal processing to collect positional information of objects beyond the Nyquist limits.

Intra- and Inter-Slice Contrastive Learning for Point Supervised OCT Fluid Segmentation

OCT fluid segmentation is a crucial task for diagnosis and therapy in ophthalmology. The current convolutional neural networks (CNNs) supervised by pixel-wise annotated masks achieve great success in OCT fluid segmentation. However, requiring pixel-wise masks from OCT images is time-consuming, expensive and expertise needed. This paper proposes an Intra- and inter-Slice Contrastive Learning Network (ISCLNet) for OCT fluid segmentation with only point supervision. Our ISCLNet learns visual representation by designing contrastive tasks that exploit the inherent similarity or dissimilarity from unlabeled OCT data. Specifically, we propose an intra-slice contrastive learning strategy to leverage the fluid-background similarity and the retinal layer-background dissimilarity. Moreover, we construct an inter-slice contrastive learning architecture to learn the similarity of adjacent OCT slices from one OCT volume. Finally, an end-to-end model combining intra- and inter-slice contrastive learning processes learns to segment fluid under the point supervision. The experimental results on two public OCT fluid segmentation datasets (i.e., AI Challenger and RETOUCH) demonstrate that the ISCLNet bridges the gap between fully-supervised and weakly-supervised OCT fluid segmentation and outperforms other well-known point-supervised segmentation methods.

Treatment responses for branch retinal vein occlusion predicted by semi-automated fluorescein angiography quantification

Backgrounds

Branch retinal vein occlusion (BRVO) is one of the most important causes of visual loss in retinal vascular diseases. The aim of this study is to predict the treatment response of anti-vascular endothelial growth factor (anti-VEGF) therapy in BRVO using semi-automated quantified fluorescein angiography (FA) features.

Methods

This retrospective case-control study enrolled patients with BRVO who are receiving anti-VEGF therapy and have been followed up for > 1 year. Those receiving < 5 anti-VEGF injections in the first year were classified as the responsive group, while those receiving ≥5 injections were the refractory group. The FA images were subjected to semi-automated pre-processing. Fluorescein leakages at the 5-min image were represented by mean gray value over parafoveal and perifoveal regions. FA leakages and central retinal thickness (CRT) on optical coherence tomography (OCT) were used for predicting the treatment response and compared using area under receiver operating characteristic curve (AUC).

Results

Eighty-nine patients (56 males, 33 females, mean age 62.5 ± 10.9 years) with BRVO were enrolled. Of the 89 eyes, 47 (53%) were in the responsive group and 42 (47%) were in the refractory group. The refractory group had a significantly higher number of anti-VEGF injections in the first year (5.9 ± 1.6 versus 2.4 ± 1.2, p < 0.001) when compared with that of the responsive group. It had thicker pre-treatment CRT (p = 0.011), post-treatment best CRT (p < 0.001) and CRT at 1-year (p < 0.001). It also had a higher mean gray value over the parafoveal (p < 0.001) and the perifoveal (p < 0.001) regions. The mean gray value over perifoveal (AUC 0.846) and parafovel (AUC 0.818) had significantly larger AUC than that of the pre-treatment OCT (AUC 0.653; p = 0.005 and p = 0.016, respectively) when predicting treatment response.

Conclusion

The refractory group had a more severe fluorescein leakage over the parafoveal and the perifoveal regions than the responsive group had. Semi-automated quantified FA leakage can be used as a biomarker for the prediction of anti-VEGF treatment response in macular edema due to BRVO.

Feature definition and comprehensive analysis on the robust identification of intraretinal cystoid regions using optical coherence tomography images

Currently, optical coherence tomography is one of the most used medical imaging modalities, offering cross-sectional representations of the studied tissues. This image modality is specially relevant for the analysis of the retina, since it is the internal part of the human body that allows an almost direct examination without invasive techniques. One of the most representative cases of use of this medical imaging modality is for the identification and characterization of intraretinal fluid accumulations, critical for the diagnosis of one of the main causes of blindness in developed countries: the Diabetic Macular Edema. The study of these fluid accumulations is particularly interesting, both from the point of view of pattern recognition and from the different branches of health sciences. As these fluid accumulations are intermingled with retinal tissues, they present numerous variants according to their severity, and change their appearance depending on the configuration of the device; they are a perfect subject for an in-depth research, as they are considered to be a problem without a strict solution. In this work, we propose a comprehensive and detailed analysis of the patterns that characterize them. We employed a pool of 11 different texture and intensity feature families (giving a total of 510 markers) which we have analyzed using three different feature selection strategies and seven complementary classification algorithms. By doing so, we have been able to narrow down and explain the factors affecting this kind of accumulations and tissue lesions by means of machine learning techniques with a pipeline specially designed for this purpose.

Recently updated global diabetic retinopathy screening guidelines: commonalities, differences, and future possibilities

Diabetic retinopathy (DR) is a global health burden. Screening for sight-threatening DR (STDR) is the first cost-effective step to decrease this burden. We analyzed the similarities and variations between the recent country-specific and the International Council of Ophthalmology (ICO) DR guideline to identify gaps and suggest possible solutions for future universal screening. We selected six representative national DR guidelines, one from each World Health Organization region, including Canada (North America), England (Europe), India (South- East Asia), Kenya (Africa), New Zealand (Western Pacific), and American Academy of Ophthalmology Preferred Practice Pattern (used in Latin America and East Mediterranean). We weighed the newer camera and artificial intelligence (AI) technology against the traditional screening methodologies. All guidelines agree that screening for DR and STDR in people with diabetes is currently led by an ophthalmologist; few engage non-ophthalmologists. Significant variations exist in the screening location and referral timelines. Screening with digital fundus photography has largely replaced traditional slit-lamp examination and ophthalmoscopy. The use of mydriatic digital 2-or 4-field fundus photography is the current norm; there is increasing interest in using non-mydriatic fundus cameras. The use of automated DR grading and tele-screening is currently sparse. Country-specific guidelines are necessary to align with national priorities and human resources. International guidelines such as the ICO DR guidelines remain useful in countries where no guidelines exist. Validation studies on AI and tele-screening call for urgent policy decisions to integrate DR screening into universal health coverage to reduce this global public health burden.

Student becomes teacher: training faster deep learning lightweight networks for automated identification of optical coherence tomography B-scans of interest using a student-teacher framework

This work explores a student-teacher framework that leverages unlabeled images to train lightweight deep learning models with fewer parameters to perform fast automated detection of optical coherence tomography B-scans of interest. Twenty-seven lightweight models (LWMs) from four families of models were trained on expert-labeled B-scans (∼70 K) as either "abnormal" or "normal", which established a baseline performance for the models. Then the LWMs were trained from random initialization using a student-teacher framework to incorporate a large number of unlabeled B-scans (∼500 K). A pre-trained ResNet50 model served as the teacher network. The ResNet50 teacher model achieved 96.0% validation accuracy and the validation accuracy achieved by the LWMs ranged from 89.6% to 95.1%. The best performing LWMs were 2.53 to 4.13 times faster than ResNet50 (0.109s to 0.178s vs. 0.452s). All LWMs benefitted from increasing the training set by including unlabeled B-scans in the student-teacher framework, with several models achieving validation accuracy of 96.0% or higher. The three best-performing models achieved comparable sensitivity and specificity in two hold-out test sets to the teacher network. We demonstrated the effectiveness of a student-teacher framework for training fast LWMs for automated B-scan of interest detection leveraging unlabeled, routinely-available data.

En face optical coherence tomography patterns in patients with angiographically documented uveitic macular edema: a cross-sectional retrospective study

PURPOSE

To elucidate the en face optical coherence tomography (OCT) patterns of macular edema in eyes with angiographically documented uveitic macular edema (UME) and compare visual acuity (VA), OCT and OCT-angiography (OCT-A) parameters among the morphological subgroups.

METHODS

Thirty-nine eyes of 29 patients with angiographically proven UME were enrolled into the study. All patients underwent comprehensive ophthalmological examination including structural OCT and OCT-A in addition to fluorescein angiography. Eyes with UME were divided into three subgroups (petaloid, sunflower and spoke-wheel pattern) with the help of en face OCT imaging.

RESULTS

Posterior uveitis was the most common type of uveitis (17 patients; 58.7%), followed by panuveitis (11 patients; 37.9%) and then intermediate uveitis (1 patient; 3.4%). Underlying causes of uveitis were Behçet's disease (13 patients; 44.8%), idiopathic (11 patients; 37.9%), rheumatoid arthritis (2 patients; 6.9%), sarcoidosis (1 patient; 3.4%), inflammatory polyarthritis (1 patient; 3.4%) and psoriatic arthritis (1 patient; 3.4%). The most common en face OCT pattern was petaloid type (25 of 39 eyes; 64.1%). Eleven eyes (28.2%) had sunflower pattern and three (7.7%) spoke-wheel pattern. There were no statistically significant difference among the subgroups regarding the age, VA, central macular thickness and vessel density.

CONCLUSION

This study reveals three morphological en face OCT patterns in eyes with UME and en face OCT may find a niche in the UME classification with the accumulation of experience among the uveitis experts.

No significant retinal damage induced by major orthopedic surgery - a pilot study

BACKGROUND

Perioperative visual loss is one of the rare but devastating complications of anesthesia and surgery. The incidence of less severe or even subclinical postoperative visual dysfunction is unknown. Therefore, we decided to perform a pilot prospective observational clinical study to evaluate whether structural changes of the retina can be detected in patients undergoing elective orthopaedic surgery by optical coherence tomography (OCT).

METHODS

Adult patients indicated for elective knee replacement surgery with the absence of known retinal or optic nerve disease were included. Each patient underwent baseline OCT examination of the eyes one day before surgery and it was repeated 4-7 days after the surgery. The surgery was done under general and epidural anesthesia.

RESULTS

A total of 18 patients (6 men and 12 women) at the age of 70.8±7.1 years were enrolled. We found statistically significant changes in the Macular central thickness and in a few areas of the Retinal Nerve Fiber Layer between the baseline and postoperative measurements.

CONCLUSIONS

Even though we found significant changes in some parameters, we did not confirm that general anesthesia and/or surgical damage causes significant damage of the retina using OCT measurement.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT04311801).

OctNET: A Lightweight CNN for Retinal Disease Classification from Optical Coherence Tomography Images

BACKGROUND AND OBJECTIVE

Retinal diseases are becoming a major health problem in recent years. Their early detection and ensuing treatment are essential to prevent visual damage, as the number of people affected by diabetes is expected to grow exponentially. Retinal diseases progress slowly, without any discernible symptoms. Optical Coherence Tomography (OCT) is a diagnostic tool capable of analyzing and identifying the quantitative discrimination in the disease affected retinal layers with high resolution. This paper proposes a deep neural network-based classifier for the computer-aided classification of Diabetic Macular Edema (DME), drusen, Choroidal NeoVascularization (CNV) from normal OCT images of the retina.

METHODS

In the proposed method, we demonstrate the feasibility of classifying and detecting severe retinal pathologies from OCT images using a deep convolutional neural network having six convolutional blocks. The classification results are explained using a gradient-based class activation mapping algorithm.

RESULTS

Training and validation of the model are performed on a public dataset of 83,484 images with expert-level disease grading of CNV, DME, and drusen, in addition to normal retinal image. We achieved a precision of 99.69%, recall of 99.69%, and accuracy of 99.69% with only three misclassifications out of 968 test cases.

CONCLUSION

In the proposed work, downsampling and weight sharing were introduced to improve the training efficiency and were found to reduce the trainable parameters significantly. The class activation mapping was also performed, and the output image was similar to the retina's actual color OCT images. The proposed network used only 6.9% of learnable parameters compared to the existing ResNet-50 model and yet outperformed it in classification. The proposed work can be potentially employed in real-time applications due to reduced complexity and fewer learnable parameters over other models.

Macular microhole and foveal red spot syndrome: a critical review of the literature

PURPOSE

The purpose of this article is to review the literature on nomenclature, natural history, clinical features, diagnosis, management, and prognosis of both macular microhole (MMH) and foveal red spot syndrome (FRS).

METHODS

A PubMed primary literature search (February 1, 2020) utilizing the terms macular microhole, foveal red spot syndrome, and outer retinal hole was conducted. All chosen articles were case reports or case series. Articles qualified for inclusion if they documented symptoms, imaging findings, or followed patients longitudinally.

RESULTS

A total of 14 studies from 1988 to 2019 that evaluated either MMH, FRS, or both were included in the review. No comparative study between the two defects was found. Studies often used the terms FRS and MMH interchangeably to reference both partial- and full-thickness lesions of the macula. Spectral-domain optical coherence tomography (SD-OCT) was most frequently able to identify these lesions and revealed an absence of all neural retinal layers from the inner limiting membrane (ILM) to the retinal pigment epithelium (RPE) in the full-thickness lesions while the partial-thickness lesions most often involved the photoreceptor layer (PRL) and less frequently the external limiting membrane (ELM). OCT revealed that vitreomacular traction (VMT) was involved in the natural history of both FRS and MMH for a significant subset of patients.

CONCLUSION

The terms MMH and FRS have been used interchangeably in the literature. Advances in OCT have revealed that MMHs and FRSs are distinct but sometimes overlapping entities. We suggest that MMH and FRS are similar entities defined as one or more sharply defined lesions in the fovea of the eye < 150 μm in size. MMHs are a full-thickness defect of the entire neuroretina at the center of the foveola while FRSs are partial-thickness lesions. Current literature suggests that there may be subtle differences in the pathogenesis, clinical features, and diagnosis between MMH and FRS; however, prognosis and management for both are favorable. Lastly, we suggest that the terms outer lamellar macular microholes and full-thickness macular microholes may be the more appropriate terminologies to refer to FRS and MMH, respectively.

Regulation of blood-retinal barrier cell-junctions in diabetic retinopathy

Loss of the blood-retinal barrier (BRB) integrity and subsequent damage to the neurovascular unit in the retina are the underlying reasons for diabetic retinopathy (DR). Damage to BRB eventually leads to severe visual impairment in the absence of prompt intervention. Diabetic macular edema and proliferative DR are the advanced stages of the disease where BRB integrity is altered. Primary mechanisms contributing to BRB dysfunction include loss of cell-cell barrier junctions, vascular endothelial growth factor, advanced glycation end products-induced damage, and oxidative stress. Although much is known about the involvement of adherens and tight-junction proteins in the regulation of vascular permeability in various diseases, there is a significant gap in our knowledge on the junctional proteins expressed in the BRB and how BRB function is modulated in the diabetic retina. In this review article, we present our current understanding of the molecular composition of BRB, the changes in the BRB junctional protein turnover in DR, and how BRB functional modulation affects vascular permeability and macular edema in the diabetic retina.

Automated quantification of macular fluid in retinal diseases and their response to anti-VEGF therapy

AIM

To objectively assess disease activity and treatment response in patients with retinal vein occlusion (RVO), neovascular age-related macular degeneration (nAMD) and centre-involved diabetic macular oedema (DME), using artificial intelligence-based fluid quantification.

METHODS

Posthoc analysis of 2311 patients (11 151 spectral-domain optical coherence tomography volumes) from five clinical, multicentre trials, who received a flexible antivascular endothelial growth factor (anti-VEGF) therapy over a 12-month period. Fluid volumes were measured with a deep learning algorithm at baseline/months 1, 2, 3 and 12, for three concentric circles with diameters of 1, 3 and 6 mm (fovea, paracentral ring and pericentral ring), as well as four sectors surrounding the fovea (superior, nasal, inferior and temporal).

RESULTS

In each disease, at every timepoint, most intraretinal fluid (IRF) per square millimetre was present at the fovea, followed by the paracentral ring and pericentral ring (p<0.0001). While this was also the case for subretinal fluid (SRF) in RVO/DME (p<0.0001), patients with nAMD showed more SRF in the paracentral ring than at the fovea up to month 3 (p<0.0001). Between sectors, patients with RVO/DME showed the highest IRF volumes temporally (p<0.001/p<0.0001). In each disease, more SRF was consistently found inferiorly than superiorly (p<0.02). At month 1/12, we measured the following median reductions of initial fluid volumes. For IRF: RVO, 95.9%/97.7%; nAMD, 91.3%/92.8%; DME, 37.3%/69.9%. For SRF: RVO, 94.7%/97.5%; nAMD, 98.4%/99.8%; DME, 86.3%/97.5%.

CONCLUSION

Fully automated localisation and quantification of IRF/SRF over time shed light on the fluid dynamics in each disease. There is a specific anatomical response of IRF/SRF to anti-VEGF therapy in all diseases studied.

Analysis of retinal nerve fiber layer birefringence in patients with glaucoma and diabetic retinopathy by polarization sensitive OCT

The retinal nerve fiber layer (RNFL) is a fibrous tissue that shows form birefringence. This optical tissue property is related to the microstructure of the nerve fiber axons that carry electrical signals from the retina to the brain. Ocular diseases that are known to cause neurologic changes, like glaucoma or diabetic retinopathy (DR), might alter the birefringence of the RNFL, which could be used for diagnostic purposes. In this pilot study, we used a state-of-the-art polarization sensitive optical coherence tomography (PS-OCT) system with an integrated retinal tracker to analyze the RNFL birefringence in patients with glaucoma, DR, and in age-matched healthy controls. We recorded 3D PS-OCT raster scans of the optic nerve head area and high-quality averaged circumpapillary PS-OCT scans, from which RNFL thickness, retardation and birefringence were derived. The precision of birefringence measurements was 0.005°/µm. As compared to healthy controls, glaucoma patients showed a slightly reduced birefringence (0.129 vs. 0.135°/µm), although not statistically significant. The DR patients, however, showed a stronger reduction of RNFL birefringence (0.103 vs. 0.135°/µm) which was highly significant. This result might open new avenues into early diagnosis of DR and related neurologic changes.

Efficacy of Intravitreal Dexamethasone Implant in Different Patterns of Diabetic Macular Edema

PURPOSE

Different patterns of diabetic macular edema (DME) suggest different pathogenesis and drug response. We evaluated the outcomes after intravitreal dexamethasone (DEX) implant for DME with or without serous retinal detachment (SRD).

METHODS

In this retrospective study, 22 naïve patients (23 eyes) with DME who underwent a single DEX implant were evaluated. Based on the optical coherence tomographic pattern of DME, 12 eyes had a cystoid macular edema pattern (Group 1) and 11 eyes had an SRD pattern (Group 2). The best-corrected visual acuity (BCVA), central retinal thickness (СRТ), central retinal volume (CRV), SRD height (SRDh), and intraocular pressure (IOP) were recorded before and at two and four months after the treatment.

RESULTS

There were no significant differences between the groups regarding demographic, clinical data and outcomes at baseline. In Group 1, the CRT and CRV significantly decreased at two months (P = 0.002 and P = 0.01, respectively), while the BCVA significantly improved at four months (P = 0.03). In Group 2, the CRT and CRV significantly improved (P < 0.01 and P ≤ 0.01, respectively) during the follow-up period. At four months, both groups showed a recurrence of DME, Group 1 in particular (two-month CRT reduction, -149 ± 127 µm vs four-month CRT reduction, -72 ± 174 µm; P = 0.04). The mean reduction in CRV was significantly different at four months (Group 1, -0.49 ± 1.7 mm3 vs Group 2, -1.3 ± 1.3 mm3 ; P = 0.04). In Group 2, the SRDh significantly decreased at two (P = 0.01) and four months (P = 0.01). Four cases with elevated IOP were managed.

CONCLUSION

DEX implants were found to be effective in different patterns of DME. The SRD pattern may predict a longer-lasting morphologic efficacy.

Infrared retinal images for flashless detection of macular edema

This study evaluates the use of infrared (IR) images of the retina, obtained without flashes of light, for machine-based detection of macular oedema (ME). A total of 41 images of 21 subjects, here with 23 cases and 18 controls, were studied. Histogram and gray-level co-occurrence matrix (GLCM) parameters were extracted from the IR retinal images. The diagnostic performance of the histogram and GLCM parameters was calculated in hindsight based on the known labels of each image. The results from the one-way ANOVA indicated there was a significant difference between ME eyes and the controls when using GLCM features, with the correlation feature having the highest area under the curve (AUC) (A_Z) value. The performance of the proposed method was also evaluated using a support vector machine (SVM) classifier that gave sensitivity and specificity of 100%. This research shows that the texture of the IR images of the retina has a significant difference between ME eyes and the controls and that it can be considered for machine-based detection of ME without requiring flashes of light.

Management of Cystoid Macular Edema After Vitrectomy for Proliferative Vitreoretinopathy

BACKGROUND AND OBJECTIVE

To assess therapies for cystoid macular edema (CME) following pars plana vitrectomy for proliferative vitreoretinopathy (PVR).

PATIENTS AND METHODS

Retrospective analysis of 42 eyes developing CME after PVR surgery. Treatments included topical therapy, sub-Tenon's triamcinolone acetonide (STTA), intravitreal bevacizumab and combinations thereof. Best-corrected visual acuity (BCVA) as well as central subfield thickness (CST) were tracked.

RESULTS

Mean Snellen BCVA improved from 20/598 to 20/297 (logMAR change -0.21; confidence interval [CI], -0.39 to -0.03; P = .03). Mean CST improved from 448 µm to 260 µm (CI, -248.70 to -126.06; P < 0.01). There was no difference in efficacy between treatment subgroups (analysis of variance, P = 0.16, 0.43), but STTA yielded statistically significant improvement in both categories (CI, -0.79 to -0.11; P = 0.01; and CI, -333.74 to 166.51; P < .01).

CONCLUSIONS

Treatment of CME following PVR surgery is possible with a variety of different options. STTA appears to yield anatomical and visual improvement. [Ophthalmic Surg Lasers Imaging Retina. 2020;51:436-443.].

Intraretinal Fluid Pattern Characterization in Optical Coherence Tomography Images

Optical Coherence Tomography (OCT) has become a relevant image modality in the ophthalmological clinical practice, as it offers a detailed representation of the eye fundus. This medical imaging modality is currently one of the main means of identification and characterization of intraretinal cystoid regions, a crucial task in the diagnosis of exudative macular disease or macular edema, among the main causes of blindness in developed countries. This work presents an exhaustive analysis of intensity and texture-based descriptors for its identification and classification, using a complete set of 510 texture features, three state-of-the-art feature selection strategies, and seven representative classifier strategies. The methodology validation and the analysis were performed using an image dataset of 83 OCT scans. From these images, 1609 samples were extracted from both cystoid and non-cystoid regions. The different tested configurations provided satisfactory results, reaching a mean cross-validation test accuracy of 92.69%. The most promising feature categories identified for the issue were the Gabor filters, the Histogram of Oriented Gradients (HOG), the Gray-Level Run-Length matrix (GLRL), and the Laws’ texture filters (LAWS), being consistently and considerably selected along all feature selector algorithms in the top positions of different relevance rankings.

Cystoid macular edema related to cataract surgery and topical prostaglandin analogs: Mechanism, diagnosis, and management

Cystoid macular edema (CME) is a form of macular retina thickening that is characterized by the appearance of cystic fluid-filled intraretinal spaces. It has classically been diagnosed upon investigation after a decrease in visual acuity; however, improvements in imaging technology make it possible to noninvasively detect CME even before a clinically significant decrease in central vision. Risk factors for the development of CME include diabetic retinopathy, retinal vein occlusion, uveitis, and cataract surgery. It has been proposed that eyes with elevated intraocular pressure after cataract surgery, including those treated with prostaglandin analog eye drops, may be at higher risk for the development of CME. We summarize the current knowledge of the molecular mechanisms underlying CME, the potential role of ocular surgery and topical glaucoma medication in increasing the risk of CME, the newly developed imaging methods for diagnosing CME, and the clinical management of CME.

Automated segmentation of macular edema in OCT using deep neural networks

Macular edema is an eye disease that can affect visual acuity. Typical disease symptoms include subretinal fluid (SRF) and pigment epithelium detachment (PED). Optical coherence tomography (OCT) has been widely used for diagnosing macular edema because of its non-invasive and high resolution properties. Segmentation for macular edema lesions from OCT images plays an important role in clinical diagnosis. Many computer-aided systems have been proposed for the segmentation. Most traditional segmentation methods used in these systems are based on low-level hand-crafted features, which require significant domain knowledge and are sensitive to the variations of lesions. To overcome these shortcomings, this paper proposes to use deep neural networks (DNNs) together with atrous spatial pyramid pooling (ASPP) to automatically segment the SRF and PED lesions. Lesions-related features are first extracted by DNNs, then processed by ASPP which is composed of multiple atrous convolutions with different fields of view to accommodate the various scales of the lesions. Based on ASPP, a novel module called stochastic ASPP (sASPP) is proposed to combat the co-adaptation of multiple atrous convolutions. A large OCT dataset provided by a competition platform called "AI Challenger" are used to train and evaluate the proposed model. Experimental results demonstrate that the DNNs together with ASPP achieve higher segmentation accuracy compared with the state-of-the-art method. The stochastic operation added in sASPP is empirically verified as an effective regularization method that can alleviate the overfitting problem and significantly reduce the validation error.

Designing visible-light optical coherence tomography towards clinics

BACKGROUND

The capabilities of visible-light optical coherence tomography (vis-OCT) in noninvasive anatomical and functional retinal imaging have been demonstrated by multiple groups in both rodents and healthy human subjects. Translating laboratory prototypes to an integrated clinical-environment-friendly system is required to explore the full potential of vis-OCT in disease management.

METHODS

We developed and optimized a portable vis-OCT system for human retinal imaging in clinical settings. We acquired raster- and circular-scan images from both healthy and diseased human eyes.

RESULTS

The new vis-OCT provided high-quality retinal images of both subjects without any known eye diseases and patients with various retinal diseases, including retinal occlusive disease and diabetic retinopathy (DR) over a broad range of ages.

CONCLUSIONS

A newly designed vis-OCT system is sufficiently optimized to be suited for routine patients' examinations in clinics. Vis-OCT has the potential to add new anatomical and functional imaging capabilities to ophthalmic clinical care.

Macular thickness amplitude changes when switching from discontinuous to continuous therapy for diabetic macular oedema

OBJECTIVE

To investigate if the mean central retinal thickness (CRT) amplitude, measured between visits, is consistently decreased when switching from discontinuous to continuous therapy for diabetic macular oedema (DME) following fluocinolone acetonide (FAc) administration.

METHODS AND ANALYSIS

In this retrospective cohort study, all patients with DME treated with FAc at a single centre were included. The primary outcome was CRT amplitude changes measured at each visit prior to and after FAc administration. Secondary outcomes included average number of DME treatments before and after FAc injection, visual acuity and intraocular pressure changes.

RESULTS

Nineteen eyes were included. The mean (SD) follow-up after FAc was 399 (222) days. The mean (SD) CRT amplitude before FAc was 194.6 (114.90) µm, and following FAc administration, the amplitude decreased to 70.8 (94.23) µm (95% CI -189.5 to -58.1; p≤0.001). After FAc, the number of treatments required per month significantly decreased from an average of 1 treatment every 2.7 months to every 6 months (p=0.009).

CONCLUSION

In patients with DME, the CRT amplitude values and number of treatments significantly decrease following FAc administration while maintaining vision. Further studies are needed to evaluate the significance of these interesting findings.

Multimodal OCT Reflectivity Analysis of the Cystoid Spaces in Cystoid Macular Edema

Purpose

To compare and evaluate images of macular cysts with different degrees of reflectivity (from gray to black signal) as observed in B scan spectral domain OCT (SDOCT) and EnFace OCT with decorrelation signal obtained with OCT-angiography (OCTA) in eyes with cystoid macular edema (CME) secondary to diabetic retinopathy (DR) and retinal vein occlusion (RVO).

Methods

Images from 3033 patients affected by CME secondary to diabetes or RVO examined OCTA (Optovue XR Avanti, Optovue, USA) at the University Eye Clinic of Créteil, Hôpital Intercommunal, France, and at the University Eye Clinic of Cagliari, “San Giovanni di Dio” Hospital, Italy, were retrospectively examined. The deep capillary plexus OCTA images and the corresponding EnFace OCT images, both acquired with the same automatic segmentation, had been overlapped to compose RGB color images as red and green channels, respectively, using ImageJ software (National Institutes of Health, Bethesda, MD). Afterward, linear regions of interest were traced on the color images to obtain the profiles of OCTA and EnFace gray values. Number of pixels, mean gray value and standard deviation of the area traced in OCT-A, and EnFace image were analyzed and statistically correlated. Data were exported to Excel to create the plots.

Results

94 patients with DME and 27 patients with RVO showed intraretinal macular cystoid spaces with similar homogeneous, gray-looking content; 73 patients with DME and 113 patients with RVO showed macular cystoid spaces with homogeneous, black-looking content, as observed at SD-OCT, EnFace and OCTA scans. Interestingly, the limits of macular cystoid spaces were clearly detectable with OCTA. The analysis of red and green profiles demonstrated a clearly visible overlap between average OCTA and EnFace signal observed around cystoid spaces that could be attributed to a relationship between the dynamic vascularization and the structural density of the tissue.

Conclusions

This is the first investigation that characterizes and correlates OCTA and EnFace signals on images of macular cystoid spaces in DR and RVO. The low intensity OCTA signals observed inside cystoid spaces raise a relevant question about their nature, as to whether they are due to the presence of corpusculated material pouring out from bloodocular-barrier or they should be considered OCTA artifacts.

Multilayered Deep Structure Tensor Delaunay Triangulation and Morphing Based Automated Diagnosis and 3D Presentation of Human Macula

Maculopathy is the group of diseases that affects central vision of a person and they are often associated with diabetes. Many researchers reported automated diagnosis of maculopathy from optical coherence tomography (OCT) images. However, to the best of our knowledge there is no literature that presents a complete 3D suite for the extraction as well as diagnosis of macula. Therefore, this paper presents a multilayered convolutional neural networks (CNN) structure tensor Delaunay triangulation and morphing based fully autonomous system that extracts up to nine retinal and choroidal layers along with the macular fluids. Furthermore, the proposed system utilizes the extracted retinal information for the automated diagnosis of maculopathy as well as for the robust reconstruction of 3D macula of retina. The proposed system has been validated on 41,921 retinal OCT scans acquired from different OCT machines and it significantly outperformed existing state of the art solutions by achieving the mean accuracy of 95.27% for extracting retinal and choroidal layers, mean dice coefficient of 0.90 for extracting fluid pathology and the overall accuracy of 96.07% for maculopathy diagnosis. To the best of our knowledge, the proposed framework is first of its kind that provides a fully automated and complete 3D integrated solution for the extraction of candidate macula along with its fully automated diagnosis against different macular syndromes.

Automatic macular edema identification and characterization using OCT images

BACKGROUND AND OBJECTIVE

The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as it provides useful information for the identification and diagnosis of the different types of Macular Edema (ME). These types are clinically defined, according to the clinical guidelines, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Their accurate identification and characterization facilitate the diagnostic process, determining the disease severity and, therefore, allowing the clinicians to achieve more precise analysis and suitable treatments.

METHODS

This paper proposes a new fully automatic system for the identification and characterization of the three types of ME using Optical Coherence Tomography (OCT) images. In the case of SRD and CME edemas, multilevel image thresholding approaches were designed and combined with the application of ad-hoc clinical restrictions. The case of DRT edemas, given their complexity and fuzzy regional appearance, was approached by a learning strategy that exploits intensity, texture and clinical-based information to identify their presence.

RESULTS

The system provided satisfactory results with F-Measures of 87.54% and 91.99% for the DRT and CME detections, respectively. In the case of SRD edemas, the system correctly detected all the cases that were included in the designed dataset.

CONCLUSIONS

The proposed methodology offered an accurate performance for the individual identification and characterization of the three different types of ME in OCT images. In fact, the method is capable to handle the ME analysis even in cases of significant severity with the simultaneous existence of the three ME types that appear merged inside the retinal layers.

Optical coherence tomography: A guide to interpretation of common macular diseases

Optical coherence tomography is a quick, non invasive and reproducible imaging tool for macular lesions and has become an essential part of retina practice. This review address the common protocols for imaging the macula, basics of image interpretation, features of common macular disorders with clues to differentiate mimickers and an introduction to choroidal imaging. It includes case examples and also a practical algorithm for interpretation.