Prediction of response to anti-vascular endothelial growth factor treatment in diabetic macular oedema using an optical coherence tomography-based machine learning method

Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration

PURPOSE

To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period.

METHODS

An extensive analysis was conducted on the use of ML to predict fibrosis and atrophy development on nAMD patients at 36 months from start of anti-VEGF treatment, using only data from the first 12 months. We use data collected according to real-world practice, which includes clinical and genetic factors.

RESULTS

The ML analysis consistently identified ETDRS as a relevant factor for predicting the development of atrophy and fibrosis, confirming previous statistical analyses. Also, it was shown that genetic variables did not demonstrate statistical relevance in the prediction. Despite the complexity of predicting macular degeneration, our model was able to obtain a balance accuracy of 63% and an AUC of 0.72 when predicting the development of atrophy or fibrosis at 36 months.

CONCLUSION

This study demonstrates the potential of ML techniques in predicting the development of fibrosis and atrophy in nAMD patients receiving long-term anti-VEGF treatment. The findings highlight the importance of clinical factors, particularly ETDRS (early treatment diabetic retinopathy study) visual acuity test, in predicting these outcomes. The lessons learned from this research can guide future ML-based prediction tasks in the field of ophthalmology and contribute to the design of data collection processes.

Effectiveness of Suprachoroidal Injection of Triamcinolone Acetonide in Diabetic Macular Edema Following Pars Plana Vitrectomy Using a Modified Custom Microneedle

Purpose

The present study evaluated the efficacy of suprachoroidal injection of Triamcinolone Acetonide (SCTA) in diabetic macular edema (DME) following pars plana vitrectomy (PPV) using a modified microneedle.

Patients and methods

A prospective interventional study was conducted on 60 eyes of 60 patients with centrally involved diabetic macular edema following pars plana vitrectomy (PPV). SCTA was performed at the baseline and repeated after 3 months in case of persistent subretinal or intraretinal fluid, central macular thickness (CMT) more than 300 µm or visual loss by more than one line of the Snellen chart.

Results

The present study detected significant reduction of the CMT from 498.3 ± 94.8 µm at the baseline to 212.3 ± 11.9 µm after 12 months of injection with p < 0.001 and a significant improvement of best corrected visual acuity (BCVA) from 1 (0.9-1.2) at the baseline to 0.5 (0.3-0.7) after 12 months of injection with p < 0.001. The intraocular pressure (IOP) increased significantly after 3 months of injection with p < 0.001 and then gradually declined to its normal level after 6 months. Inner segment/outer segment (IS/OS) disruption was the only significant predictor of the final CMT; however, the number of injections, IS/OS disruption, baseline BCVA and the HbA1C level were the significant predictors of the final BCVA after injection.

Conclusion

Suprachoroidal injection of TA using this microneedle resulted in significant anatomical and functional improvement in previously vitrectomized diabetic macular edema patients with no recorded ocular or systemic adverse events.

Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

Topic

To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.

Clinical relevance

Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.

Methods

The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).

Results

There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.

Conclusion

With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.

Financial Disclosures

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

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema

BACKGROUND

Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges.

MAIN TEXT

This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications.

CONCLUSION

AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

Predicting Visual Acuity Responses to Anti-VEGF Treatment in the Comparison of Age-related Macular Degeneration Treatments Trials Using Machine Learning

PURPOSE

To evaluate multiple machine learning (ML) models for predicting 2-year visual acuity (VA) responses to anti-vascular endothelial growth factor (anti-VEGF) treatment in the Comparison of Age-related Macular Degeneration (AMD) Treatments Trials (CATT) for patients with neovascular AMD (nAMD).

DESIGN

Secondary analysis of public data from a randomized clinical trial.

PARTICIPANTS

A total of 1029 CATT participants who completed 2 years of follow-up with untreated active nAMD and baseline VA between 20/25 and 20/320 in the study eye.

METHODS

Five ML models (support vector machine, random forest, extreme gradient boosting, multilayer perceptron neural network, and lasso) were applied to clinical and image data from baseline and weeks 4, 8, and 12 for predicting 4 VA outcomes (≥ 15-letter VA gain, ≥ 15-letter VA loss, VA change from baseline, and actual VA) at 2 years. The CATT data from 1029 participants were randomly split for training (n = 717), from which the models were trained using 10-fold cross-validation, and for final validation on a test data set (n = 312).

MAIN OUTCOME MEASURES

Performances of ML models were assessed by R2 and mean absolute error (MAE) for predicting VA change from baseline and actual VA at 2 years, by the area under the receiver operating characteristic curve (AUC) for predicting ≥ 15-letter VA gain and loss from baseline.

RESULTS

Using training data up to week 12, the ML models from cross-validation achieved mean R2 of 0.24 to 0.29 (MAE = 9.1-9.8 letters) for predicting VA change and 0.37 to 0.41 (MAE = 9.3-10.2 letters) for predicting actual VA at 2 years. The mean AUCs for predicting ≥ 15-letter VA gain and loss at 2 years was 0.84 to 0.85 and 0.58 to 0.73, respectively. In final validation on the test data set up to week 12, the models had an R2 of 0.33 to 0.38 (MAE = 8.9-9.9 letters) for predicting VA change, an R2 of 0.37 to 0.45 (MAE = 8.8-10.2 letters) for predicting actual VA at 2 years, and AUCs of 0.85 to 0.87 and 0.67 to 0.79 for predicting ≥ 15-letter VA gain and loss, respectively.

CONCLUSIONS

Machine learning models have the potential to predict 2-year VA response to anti-VEGF treatment using clinical and imaging features from the loading dose phase, which can aid in decision-making around treatment protocols for patients with nAMD.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.

Safety and Efficacy of Dexamethasone Intravitreal Implant Given Either First-Line or Second-Line in Diabetic Macular Edema

Diabetic macular edema (DME) is a common sight-threatening complication of diabetic retinopathy (DR) and the leading cause of severe visual impairment among the working-age population. Several therapeutic options are available for the management of DME, including intravitreal corticosteroids. They have been traditionally used as second-line treatment, due to the risk of intraocular pressure increase and cataract-related adverse events. However, attention has recently been focused on the primary or early use of intravitreal corticosteroids, due to growing evidence of the crucial role of inflammation in the pathogenesis of DME. Furthermore, intravitreal steroid implants offer the additional advantage of a longer duration of action compared to anti-vascular endothelial growth factor agents (anti-VEGF). This review aims to summarize the available evidence on the efficacy and safety profile of dexamethasone (DEX) intravitreal implant, with a specific focus on clinical scenarios in which it might be considered or even preferred as first-line treatment option by adequate selection of patients, considering both advantages and possible adverse events. Patients with contraindications to anti-VEGF, DME with high inflammatory OCT biomarkers, pseudophakic patients and phakic patients' candidates to cataract surgery as well as vitrectomized eyes may all benefit from first-line DEX implant. Additionally, DME not responders to anti-VEGF should be considered for a switch to DEX implant and a combination therapy of DEX implant and anti-VEGF could be a valid option in severe and persistent DME.

Automatic Diagnosis of Infectious Keratitis Based on Slit Lamp Images Analysis

Infectious keratitis (IK) is a common ophthalmic emergency that requires prompt and accurate treatment. This study aimed to propose a deep learning (DL) system based on slit lamp images to automatically screen and diagnose infectious keratitis. This study established a dataset of 2757 slit lamp images from 744 patients, including normal cornea, viral keratitis (VK), fungal keratitis (FK), and bacterial keratitis (BK). Six different DL algorithms were developed and evaluated for the classification of infectious keratitis. Among all the models, the EffecientNetV2-M showed the best classification ability, with an accuracy of 0.735, a recall of 0.680, and a specificity of 0.904, which was also superior to two ophthalmologists. The area under the receiver operating characteristics curve (AUC) of the EffecientNetV2-M was 0.85; correspondingly, 1.00 for normal cornea, 0.87 for VK, 0.87 for FK, and 0.64 for BK. The findings suggested that the proposed DL system could perform well in the classification of normal corneas and different types of infectious keratitis, based on slit lamp images. This study proves the potential of the DL model to help ophthalmologists to identify infectious keratitis and improve the accuracy and efficiency of diagnosis.

The Role of Intravitreal Corticosteroids in the Treatment of DME: Predictive OCT Biomarkers

This work aims to summarize predictive biomarkers to guide treatment choice in DME. Intravitreal anti-VEGF is considered the gold standard treatment for centers involving DME, while intravitreal steroid treatment has been established as a second-line treatment in DME. However, more than 1/3 of the patients do not adequately respond to anti-VEGF treatment despite up to 4-weekly injections. Not surprisingly, insufficient response to anti-VEGF therapy has been linked to low-normal VEGF levels in the serum and aqueous humor. These patients may well benefit from an early switch to intravitreal steroid treatment. In these patients, morphological biomarkers visible in OCT may predict treatment response and guide treatment decisions. Namely, the presence of a large amount of retinal and choroidal hyperreflective foci, disruption of the outer retinal layers and other signs of chronicity such as intraretinal cysts extending into the outer retina and a lower choroidal vascular index are all signs suggestive of a favorable treatment response of steroids compared to anti-VEGF. This paper summarizes predictive biomarkers in DME in order to assist individual treatment decisions in DME. These markers will help to identify DME patients who may benefit from primary dexamethasone treatment or an early switch.

The Fundus Structural and Functional Predictions of DME Patients After Anti-VEGF Treatments

Diabetic retinopathy (DR) is an important complication with a high incidence of 34.6% in the diabetic populations. DR could finally lead to vision impairment without effective interventions, during which, diabetic macular edema (DME) is a key phase causing visual loss. Up to date, antivascular endothelial growth factor (anti-VEGF) therapy is the first-line treatment for DME which has achieved relatively better clinical outcomes than traditional treatments. However, there are several kinds of anti-VEGF medicines, and patients are sensitive to different anti-VEGF treatments. In addition, its effectiveness is unstable. Considering the patients' need to accept continual anti-VEGF treatments and its price is comparatively high, it is clinically important to predict the prognosis after different anti-VEGF treatments. In our research, we used the demographic and clinical data of 254 DME patients and 2,763 optical coherence tomography (OCT) images from three countries to predict the fundus structural and functional parameters and treatment plan in 6 months after different anti-VEGF treatments. Eight baseline features combined with 11 models were applied to conduct seven prediction tasks. Accuracy (ACC), the area under curve (AUC), mean absolute error (MAE), and mean square error (MSE) were respectively used to evaluate the classification and regression tasks. The ACC and AUC of structural predictions of retinal pigment epithelial detachment were close to 1.000. The MAE and MSE of visual acuity predictions were nearly 0.3 to 0.4 logMAR. The ACC of treatment plan regarding continuous injection was approaching 70%. Our research has achieved great performance in the predictions of fundus structural and functional parameters as well as treatment plan, which can help ophthalmologists improve the treatment compliance of DME patients.

Deep Learning Prediction of Response to Anti-VEGF among Diabetic Macular Edema Patients: Treatment Response Analyzer System (TRAS)

Diabetic macular edema (DME) is the most common cause of visual impairment among patients with diabetes mellitus. Anti-vascular endothelial growth factors (Anti-VEGFs) are considered the first line in its management. The aim of this research has been to develop a deep learning (DL) model for predicting response to intravitreal anti-VEGF injections among DME patients. The research included treatment naive DME patients who were treated with anti-VEGF. Patient’s pre-treatment and post-treatment clinical and macular optical coherence tomography (OCT) were assessed by retina specialists, who annotated pre-treatment images for five prognostic features. Patients were also classified based on their response to treatment in their post-treatment OCT into either good responder, defined as a reduction of thickness by >25% or 50 µm by 3 months, or poor responder. A novel modified U-net DL model for image segmentation, and another DL EfficientNet-B3 model for response classification were developed and implemented for predicting response to anti-VEGF injections among patients with DME. Finally, the classification DL model was compared with different levels of ophthalmology residents and specialists regarding response classification accuracy. The segmentation deep learning model resulted in segmentation accuracy of 95.9%, with a specificity of 98.9%, and a sensitivity of 87.9%. The classification accuracy of classifying patients’ images into good and poor responders reached 75%. Upon comparing the model’s performance with practicing ophthalmology residents, ophthalmologists and retina specialists, the model’s accuracy is comparable to ophthalmologist’s accuracy. The developed DL models can segment and predict response to anti-VEGF treatment among DME patients with comparable accuracy to general ophthalmologists. Further training on a larger dataset is nonetheless needed to yield more accurate response predictions.

Quantitative Assessment of Fundus Tessellated Density and Associated Factors in Fundus Images Using Artificial Intelligence

Purpose

This study aimed to quantitative assess the fundus tessellated density (FTD) and associated factors on the basis of fundus photographs using artificial intelligence.

Methods

A detailed examination of 3468 individuals was performed. The proposed method for FTD measurements consists of image preprocessing, sample labeling, deep learning segmentation model, and FTD calculation. Fundus tessellation was extracted as region of interest and then the FTD could be obtained by calculating the average exposed choroid area of per unit area of fundus. Besides, univariate and multivariate linear regression analysis have been conducted for the statistical analysis.

Results

The mean FTD was 0.14 ± 0.08 (median, 0.13; range, 0–0.39). In multivariate analysis, FTD was significantly (P < 0.001) associated with thinner subfoveal choroidal thickness, longer axial length, larger parapapillary atrophy, older age, male sex and lower body mass index. Correlation analysis suggested that the FTD increased by 33.1% (r = 0.33, P < .001) for each decade of life. Besides, correlation analysis indicated the negative correlation between FTD and spherical equivalent (SE) in the myopia participants (r = −0.25, P < 0.001), and no correlations between FTD and SE in the hypermetropia and emmetropic participants.

Conclusions

It is feasible and efficient to extract FTD information from fundus images by artificial intelligence–based imaging processing. FTD can be widely used in population screening as a new quantitative biomarker for the thickness of the subfoveal choroid. The association between FTD with pathological myopia and lower visual acuity warrants further investigation.

Translational Relevance

Artificial intelligence can extract valuable clinical biomarkers from fundus images and assist in population screening.

Rho-Kinase Inhibitors for the Treatment of Refractory Diabetic Macular Oedema

Diabetic macular oedema (DMO) is one of the leading causes of vision loss associated with diabetic retinopathy (DR). New insights in managing this condition have changed the paradigm in its treatment, with intravitreal injections of antivascular endothelial growth factor (anti-VEGF) having become the standard therapy for DMO worldwide. However, there is no single standard therapy for all patients DMO refractory to anti-VEGF treatment; thus, further investigation is still needed. The key obstacles in developing suitable therapeutics for refractory DMO lie in its complex pathophysiology; therefore, there is an opportunity for further improvements in the progress and applications of new drugs. Previous studies have indicated that Rho-associated kinase (Rho-kinase/ROCK) is an essential molecule in the pathogenesis of DMO. This is why the Rho/ROCK signalling pathway has been proposed as a possible target for new treatments. The present review focuses on the recent progress on the possible role of ROCK and its therapeutic potential in DMO. A systematic literature search was performed, covering the years 1991 to 2021, using the following keywords: "rho-Associated Kinas-es", "Diabetic Retinopathy", "Macular Edema", "Ripasudil", "Fasudil" and "Netarsudil". Better insight into the pathological role of Rho-kinase/ROCK may lead to the development of new strategies for refractory DMO treatment and prevention.

Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network

PURPOSE

To predict short-term anti-vascular endothelial growth factor (anti-VEGF) treatment responder/non-responder for neovascular age-related macular degeneration (nAMD) patients based on optical coherence tomography (OCT) images.

METHODS

A total of 4944 OCT scans from 206 patients with nAMD were involved to develop and evaluate a responder/non-responder prediction method for the short-term effect of anti-VEGF therapy. A deep learning architecture named sensitive structure guided network (SSG-Net) was proposed to make the prediction leveraging a sensitive structure guidance module trained from pre- and post-treatment images. To verify its clinical efficiency, other 2 deep learning methods and 4 experienced ophthalmologists were involved to evaluate the performance of the developed model.

RESULTS

For the testing dataset, SSG-Net could predict the response by an accuracy of 84.6% and an area under the receiver curve (AUC) of 0.83, with a sensitivity of 0.692 and specificity of 1. In contrast, the 2 compared deep learning methods achieved an accuracy of 65.4% with a sensitivity of 0.461 and specificity of 0.846, and an accuracy of 73.1% with a sensitivity of 0.692 and specificity of 0.846, respectively. The predicted accuracy for 4 experienced ophthalmologists was 53.8 to 76.9%, with sensitivity of 0.538 to 0.923 and specificity of 0.385 to 0.846, respectively.

CONCLUSION

Our proposed SSG-Net shows effective prediction on the short-term efficacy of anti-VEGF treatment for nAMD patients. This technique could potentially help clinicians explain the necessity of anti-VEGF treatment to the potential responder and avoid unnecessary treatment for the non-responder.

Deep Learning-Based Estimation of Axial Length and Subfoveal Choroidal Thickness From Color Fundus Photographs

This study aimed to develop an automated computer-based algorithm to estimate axial length and subfoveal choroidal thickness (SFCT) based on color fundus photographs. In the population-based Beijing Eye Study 2011, we took fundus photographs and measured SFCT by optical coherence tomography (OCT) and axial length by optical low-coherence reflectometry. Using 6394 color fundus images taken from 3468 participants, we trained and evaluated a deep-learning-based algorithm for estimation of axial length and SFCT. The algorithm had a mean absolute error (MAE) for estimating axial length and SFCT of 0.56 mm [95% confidence interval (CI): 0.53,0.61] and 49.20 μm (95% CI: 45.83,52.54), respectively. Estimated values and measured data showed coefficients of determination of r 2 = 0.59 (95% CI: 0.50,0.65) for axial length and r 2 = 0.62 (95% CI: 0.57,0.67) for SFCT. Bland-Altman plots revealed a mean difference in axial length and SFCT of -0.16 mm (95% CI: -1.60,1.27 mm) and of -4.40 μm (95% CI, -131.8,122.9 μm), respectively. For the estimation of axial length, heat map analysis showed that signals predominantly from overall of the macular region, the foveal region, and the extrafoveal region were used in the eyes with an axial length of < 22 mm, 22-26 mm, and > 26 mm, respectively. For the estimation of SFCT, the convolutional neural network (CNN) used mostly the central part of the macular region, the fovea or perifovea, independently of the SFCT. Our study shows that deep-learning-based algorithms may be helpful in estimating axial length and SFCT based on conventional color fundus images. They may be a further step in the semiautomatic assessment of the eye.

Factors associated with 1-year visual response following intravitreal bevacizumab treatment for diabetic macular edema: a retrospective single center study

Background

To explore the association of clinical characteristics and retinal microstructural features on optical coherence tomography in predicting 1-year visual response following intravitreal bevacizumab injections in eyes with visual impairment from center-involved diabetic macular edema (CI-DME).

Methods

Medical records of patients with visual impairment from CI-DME, who initiated intravitreal bevacizumab injections between Jan 2012 and Dec 2016 and were followed for a minimum of 12 months were retrospectively reviewed.

Results

The study included 226 eyes with a mean (SD) baseline visual acuity (VA) of 51.8 (19.1) letters. At week 12, following the three initial treatments, a mean (SD) VA improved to 61.7 (17.8) letters. Visual gain ≥ 10 letters was observed in 109 eyes (48.2%), while a limited early visual gain < 5 letters was noted in 80 eyes (35.4%). At one year, 110 eyes (48.7%) achieved a good VA gain ≥ 10 letters. In addition, eyes with poor baseline VA had a higher proportion of eyes that obtained limited early VA gained at week 12 (< 5 letters) and maintained in this visual response category at moth 12 compared to eyes with better baseline VA (74.1% versus 59.1%). In the multivariable logistic regression, the following factors reduced the probability of 1-year visual gain ≥ 10 letters: elderly (p = 0.040), better baseline vision (p = 0.001), and limited early visual gain < 5 letters at week 12 (p < 0.001). In multivariable linear regression, male (p = 0.010) and eyes with the presence of hyperreflective foci on baseline OCT (p = 0.010) were likely to have higher VA improvement. However, eyes with better baseline VA (p = 0.002), limited early VA gain at week 12 (p < 0.001), and a presence of EZ disruption at week 12 (p = 0.002) were likely to have less VA improvement.

Conclusions

Although bevacizumab is considered as effective management for CI-DME, variability in treatment responses is expected. This study revealed that baseline characteristics and visual responses at week 12 might help predict the long-term treatment response. Eyes with characteristics at risk of limited long-term visual outcome may require attention in optimizing their individual treatment strategies.