METformin for the MINimization of Geographic Atrophy Progression (METforMIN): A Randomized Trial

Purpose

Metformin use has been associated with a decreased risk of age-related macular degeneration (AMD) progression in observational studies. We aimed to evaluate the efficacy of oral metformin for slowing geographic atrophy (GA) progression.

Design

Parallel-group, multicenter, randomized phase II clinical trial.

Participants

Participants aged ≥ 55 years without diabetes who had GA from atrophic AMD in ≥ 1 eye.

Methods

We enrolled participants across 12 clinical centers and randomized participants in a 1:1 ratio to receive oral metformin (2000 mg daily) or observation for 18 months. Fundus autofluorescence imaging was obtained at baseline and every 6 months.

Main Outcome Measures

The primary efficacy endpoint was the annualized enlargement rate of the square root-transformed GA area. Secondary endpoints included best-corrected visual acuity (BCVA) and low luminance visual acuity (LLVA) at each visit.

Results

Of 66 enrolled participants, 34 (57 eyes) were randomized to the observation group and 32 (53 eyes) were randomized to the treatment group. The median follow-up duration was 13.9 and 12.6 months in the observation and metformin groups, respectively. The mean ± standard error annualized enlargement rate of square root transformed GA area was 0.35 ± 0.04 mm/year in the observation group and 0.42 ± 0.04 mm/year in the treatment group (risk difference = 0.07 mm/year, 95% confidence interval = -0.05 to 0.18 mm/year; P = 0.26). The mean ± standard error decline in BCVA was 4.8 ± 1.7 letters/year in the observation group and 3.4 ± 1.1 letters/year in the treatment group (P = 0.56). The mean ± standard error decline in LLVA was 7.3 ± 2.5 letters/year in the observation group and 0.8 ± 2.2 letters/year in the treatment group (P = 0.06). Fourteen participants in the metformin group experienced nonserious adverse events related to metformin, with gastrointestinal side effects as the most common. No serious adverse events were attributed to metformin.

Conclusions

The results of this trial as conducted do not support oral metformin having effects on reducing the progression of GA. Additional placebo-controlled trials are needed to explore the role of metformin for AMD, especially for earlier stages of the disease.

Financial Disclosures

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

Quantitative Characterization of Retinal Features in Translated OCTA

Purpose

This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware.

Methods

The method involved a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. This framework is designed to enhance the accuracy, resolution, and continuity of vascular regions in the translated OCTA (TR-\OCTA) images. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease status, to validate the quality of TR-OCTA images. The validation employs several quality and quantitative metrics to compare the translated images with ground truth OCTAs (GT-OCTA).

Result

TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution, moderate structural similarity and contrast quality compared to GT-OCTAs). There were slight discrepancies in vascular metrics, especially in diseased patients. Blood vessel features like tortuosity and vessel perimeter index showed better trend compared to density feature which is affected by local vascular distortions.

Conclusion

This study presents a promising solution to the limitations of OCTA adoption in clinical practice by using ML to translate OCT data into OCTA images.

Translation relevance

This study has the potential to significantly enhance the diagnostic process for retinal diseases by making detailed vascular imaging more widely available and reducing dependency on costly OCTA equipment.

TENAYA and LUCERNE: Two-Year Results from the Phase 3 Neovascular Age-Related Macular Degeneration Trials of Faricimab with Treat-and-Extend Dosing in Year 2

PURPOSE

To evaluate 2-year efficacy, durability, and safety of the bispecific antibody faricimab, which inhibits both angiopoietin-2 and VEGF-A.

DESIGN

TENAYA (ClinicalTrials.gov identifier, NCT03823287) and LUCERNE (ClinicalTrials.gov identifier, NCT03823300) were identically designed, randomized, double-masked, active comparator-controlled phase 3 noninferiority trials.

PARTICIPANTS

Treatment-naive patients with neovascular age-related macular degeneration (nAMD) 50 years of age or older.

METHODS

Patients were randomized (1:1) to intravitreal faricimab 6.0 mg up to every 16 weeks (Q16W) or aflibercept 2.0 mg every 8 weeks (Q8W). Faricimab fixed dosing based on protocol-defined disease activity at weeks 20 and 24 up to week 60, followed up to week 108 by a treat-and-extend personalized treatment interval regimen.

MAIN OUTCOME MEASURES

Efficacy analyses included change in best-corrected visual acuity (BCVA) from baseline at 2 years (averaged over weeks 104, 108, and 112) and proportion of patients receiving Q16W, every 12 weeks (Q12W), and Q8W dosing at week 112 in the intention-to-treat population. Safety analyses included ocular adverse events (AEs) in the study eye through study end at week 112.

RESULTS

Of 1326 patients treated across TENAYA/LUCERNE, 1113 (83.9%) completed treatment (n = 555 faricimab; n = 558 aflibercept). The BCVA change from baseline at 2 years was comparable between faricimab and aflibercept groups in TENAYA (adjusted mean change, +3.7 letters [95% confidence interval (CI), +2.1 to +5.4] and +3.3 letters [95% CI, +1.7 to +4.9], respectively; mean difference, +0.4 letters [95% CI, -1.9 to +2.8]) and LUCERNE (adjusted mean change, +5.0 letters [95% CI, +3.4 to +6.6] and +5.2 letters [95% CI, +3.6 to +6.8], respectively; mean difference, -0.2 letters [95% CI, -2.4 to +2.1]). At week 112 in TENAYA and LUCERNE, 59.0% and 66.9%, respectively, achieved Q16W faricimab dosing, increasing from year 1, and 74.1% and 81.2%, achieved Q12W or longer dosing. Ocular AEs in the study eye were comparable between faricimab and aflibercept groups in TENAYA (55.0% and 56.5% of patients, respectively) and LUCERNE (52.9% and 47.5% of patients, respectively) through week 112.

CONCLUSIONS

Treat-and-extend faricimab treatment based on nAMD disease activity maintained vision gains through year 2, with most patients achieving extended dosing intervals.

FINANCIAL DISCLOSURE(S)

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

Federated learning for diagnosis of age-related macular degeneration

This paper presents a federated learning (FL) approach to train deep learning models for classifying age-related macular degeneration (AMD) using optical coherence tomography image data. We employ the use of residual network and vision transformer encoders for the normal vs. AMD binary classification, integrating four unique domain adaptation techniques to address domain shift issues caused by heterogeneous data distribution in different institutions. Experimental results indicate that FL strategies can achieve competitive performance similar to centralized models even though each local model has access to a portion of the training data. Notably, the Adaptive Personalization FL strategy stood out in our FL evaluations, consistently delivering high performance across all tests due to its additional local model. Furthermore, the study provides valuable insights into the efficacy of simpler architectures in image classification tasks, particularly in scenarios where data privacy and decentralization are critical using both encoders. It suggests future exploration into deeper models and other FL strategies for a more nuanced understanding of these models' performance. Data and code are available at https://github.com/QIAIUNCC/FL_UNCC_QIAI.

Optimizing the OCTA layer fusion option for deep learning classification of diabetic retinopathy

The purpose of this study is to evaluate layer fusion options for deep learning classification of optical coherence tomography (OCT) angiography (OCTA) images. A convolutional neural network (CNN) end-to-end classifier was utilized to classify OCTA images from healthy control subjects and diabetic patients with no retinopathy (NoDR) and non-proliferative diabetic retinopathy (NPDR). For each eye, three en-face OCTA images were acquired from the superficial capillary plexus (SCP), deep capillary plexus (DCP), and choriocapillaris (CC) layers. The performances of the CNN classifier with individual layer inputs and multi-layer fusion architectures, including early-fusion, intermediate-fusion, and late-fusion, were quantitatively compared. For individual layer inputs, the superficial OCTA was observed to have the best performance, with 87.25% accuracy, 78.26% sensitivity, and 90.10% specificity, to differentiate control, NoDR, and NPDR. For multi-layer fusion options, the best option is the intermediate-fusion architecture, which achieved 92.65% accuracy, 87.01% sensitivity, and 94.37% specificity. To interpret the deep learning performance, the Gradient-weighted Class Activation Mapping (Grad-CAM) was utilized to identify spatial characteristics for OCTA classification. Comparative analysis indicates that the layer data fusion options can affect the performance of deep learning classification, and the intermediate-fusion approach is optimal for OCTA classification of DR.

COMPARATIVE ANALYSIS OF OCT AND OCT ANGIOGRAPHY CHARACTERISTICS IN EARLY DIABETIC RETINOPATHY

PURPOSE

To assess the quantitative characteristics of optical coherence tomography (OCT) and OCT angiography (OCTA) for the objective detection of early diabetic retinopathy (DR).

METHODS

This was a retrospective and cross-sectional study, which was carried out at a tertiary academic practice with a subspecialty. Twenty control participants, 15 people with diabetics without retinopathy (NoDR), and 22 people with mild nonproliferative diabetic retinopathy (NPDR) were included in this study. Quantitative OCT characteristics were derived from the photoreceptor hyperreflective bands, i.e., inner segment ellipsoid (ISe) and retinal pigment epithelium (RPE). OCTA characteristics, including vessel diameter index (VDI), vessel perimeter index (VPI), and vessel skeleton density (VSD), were evaluated.

RESULTS

Quantitative OCT analysis indicated that the ISe intensity was significantly trending downward with DR advancement. Comparative OCTA revealed VDI, VPI, and VSD as the most sensitive characteristics of DR. Correlation analysis of OCT and OCTA characteristics revealed weak variable correlation between the two imaging modalities.

CONCLUSION

Quantitative OCT and OCTA analyses revealed photoreceptor and vascular distortions in early DR. Comparative analysis revealed that the OCT intensity ratio, ISe/RPE, has the best sensitivity for early DR detection. Weak variable correlation of the OCT and OCTA characteristics suggests that OCT and OCTA are providing supplementary information for DR detection and classification.

An open-source deep learning network AVA-Net for arterial-venous area segmentation in optical coherence tomography angiography

BACKGROUND

Differential artery-vein (AV) analysis in optical coherence tomography angiography (OCTA) holds promise for the early detection of eye diseases. However, currently available methods for AV analysis are limited for binary processing of retinal vasculature in OCTA, without quantitative information of vascular perfusion intensity. This study is to develop and validate a method for quantitative AV analysis of vascular perfusion intensity.

METHOD

A deep learning network AVA-Net has been developed for automated AV area (AVA) segmentation in OCTA. Seven new OCTA features, including arterial area (AA), venous area (VA), AVA ratio (AVAR), total perfusion intensity density (T-PID), arterial PID (A-PID), venous PID (V-PID), and arterial-venous PID ratio (AV-PIDR), were extracted and tested for early detection of diabetic retinopathy (DR). Each of these seven features was evaluated for quantitative evaluation of OCTA images from healthy controls, diabetic patients without DR (NoDR), and mild DR.

RESULTS

It was observed that the area features, i.e., AA, VA and AVAR, can reveal significant differences between the control and mild DR. Vascular perfusion parameters, including T-PID and A-PID, can differentiate mild DR from control group. AV-PIDR can disclose significant differences among all three groups, i.e., control, NoDR, and mild DR. According to Bonferroni correction, the combination of A-PID and AV-PIDR can reveal significant differences in all three groups.

CONCLUSIONS

AVA-Net, which is available on GitHub for open access, enables quantitative AV analysis of AV area and vascular perfusion intensity. Comparative analysis revealed AV-PIDR as the most sensitive feature for OCTA detection of early DR. Ensemble AV feature analysis, e.g., the combination of A-PID and AV-PIDR, can further improve the performance for early DR assessment.

Contrastive learning-based pretraining improves representation and transferability of diabetic retinopathy classification models

Diabetic retinopathy (DR) is a major cause of vision impairment in diabetic patients worldwide. Due to its prevalence, early clinical diagnosis is essential to improve treatment management of DR patients. Despite recent demonstration of successful machine learning (ML) models for automated DR detection, there is a significant clinical need for robust models that can be trained with smaller cohorts of dataset and still perform with high diagnostic accuracy in independent clinical datasets (i.e., high model generalizability). Towards this need, we have developed a self-supervised contrastive learning (CL) based pipeline for classification of referable vs non-referable DR. Self-supervised CL based pretraining allows enhanced data representation, therefore, the development of robust and generalized deep learning (DL) models, even with small, labeled datasets. We have integrated a neural style transfer (NST) augmentation in the CL pipeline to produce models with better representations and initializations for the detection of DR in color fundus images. We compare our CL pretrained model performance with two state of the art baseline models pretrained with Imagenet weights. We further investigate the model performance with reduced labeled training data (down to 10 percent) to test the robustness of the model when trained with small, labeled datasets. The model is trained and validated on the EyePACS dataset and tested independently on clinical datasets from the University of Illinois, Chicago (UIC). Compared to baseline models, our CL pretrained FundusNet model had higher area under the receiver operating characteristics (ROC) curve (AUC) (CI) values (0.91 (0.898 to 0.930) vs 0.80 (0.783 to 0.820) and 0.83 (0.801 to 0.853) on UIC data). At 10 percent labeled training data, the FundusNet AUC was 0.81 (0.78 to 0.84) vs 0.58 (0.56 to 0.64) and 0.63 (0.60 to 0.66) in baseline models, when tested on the UIC dataset. CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.

Normalized Blood Flow Index in Optical Coherence Tomography Angiography Provides a Sensitive Biomarker of Early Diabetic Retinopathy

Purpose

To evaluate the sensitivity of normalized blood flow index (NBFI) for detecting early diabetic retinopathy (DR).

Methods

Optical coherence tomography angiography (OCTA) images of healthy controls, diabetic patients without DR (NoDR), and patients with mild nonproliferative DR (NPDR) were analyzed in this study. The OCTA images were centered on the fovea and covered a 6 mm × 6 mm area. Enface projections of the superficial vascular plexus (SVP) and the deep capillary plexus (DCP) were obtained for the quantitative OCTA feature analysis. Three quantitative OCTA features were examined: blood vessel density (BVD), blood flow flux (BFF), and NBFI. Each feature was calculated from both the SVP and DCP and their sensitivities to distinguish the three cohorts of the study were evaluated.

Results

The only quantitative feature capable of distinguishing all three cohorts was NBFI in the DCP image. Comparative study revealed that both BVD and BFF were able to distinguish the controls and NoDR from mild NPDR. However, neither BVD nor BFF was sensitive enough to separate NoDR from the healthy controls.

Conclusions

The NBFI has been demonstrated as a sensitive biomarker of early DR, revealing retinal blood flow abnormality better than traditional BVD and BFF. The NBFI in the DCP was verified as the most sensitive biomarker, supporting that diabetes affects the DCP earlier than SVP in DR.

Translational Relevance

NBFI provides a robust biomarker for quantitative analysis of DR-caused blood flow abnormalities, promising early detection and objective classification of DR.

Simulation of long-term impact of intravitreal anti-VEGF therapy on patients with severe non-proliferative diabetic retinopathy

OBJECTIVE

A simulation model was constructed to assess long-term outcomes of proactively treating severe non-proliferative diabetic retinopathy (NPDR) with anti-vascular endothelial growth factor (anti-VEGF) therapy versus delaying treatment until PDR develops.

METHODS AND ANALYSIS

Simulated patients were generated using a retrospective real-world cohort of treatment-naive patients identified in an electronic medical records database (IBM Explorys) between 2011 and 2017. Impact of anti-VEGF treatment was derived from clinical trial data for intravitreal aflibercept (PANORAMA) and ranibizumab (RISE/RIDE), averaged by weighted US market share. Real-world risk of PDR progression was modelled using Cox multivariable regression. The Monte Carlo simulation model examined rates of progression to PDR and sustained blindness (visual acuity <20/200) for 2 million patients scaled to US NPDR disease prevalence. Simulated progression rates from severe NPDR to PDR over 5 years and blindness rates over 10 years were compared for delayed versus early-treatment patients.

RESULTS

Real-world data from 77 454 patients with mild-to-severe NPDR simulated 2 million NPDR patients, of which 86 680 had severe NPDR. Early treatment of severe NPDR with anti-VEGF therapy led to a 51.7% relative risk reduction in PDR events over 5 years (15 704 early vs 32 488 delayed), with a 19.4% absolute risk reduction (18.1% vs 37.5%). Sustained blindness rates at 10 years were 4.4% for delayed and 1.9% for early treatment of severe NPDR.

CONCLUSION

The model suggests treating severe NPDR early with anti-VEGF therapy, rather than delaying treatment until PDR develops, could significantly reduce PDR incidence over 5 years and sustained blindness over 10 years.

Visual Field Abnormalities in Early-Stage Diabetic Retinopathy Assessed by Chromatic Perimetry

Purpose

The purpose of this study was to define the nature and extent of sensitivity loss using chromatic perimetry in diabetics who have mild or no retinopathy.

Methods

Thirty-four individuals with type II diabetes mellitus who have mild nonproliferative diabetic retinopathy (MDR; N = 17) or no diabetic retinopathy (NDR; N = 17) and 15 visually normal, non-diabetic controls participated. Sensitivity was assessed along the horizontal visual field meridian using an Octopus 900 perimeter. Measurements were performed under light- and dark-adapted conditions using long-wavelength (red) and short-wavelength (blue) Goldmann III targets. Cumulative defect curves (CDCs) were constructed to determine whether field sensitivity loss was diffuse or localized.

Results

Sensitivity was reduced significantly under light-adapted conditions for both stimulus colors for the NDR (mean defect ± SEM = -2.1 dB ± 0.6) and MDR (mean defect ± SEM = -4.0 dB ± 0.7) groups. Sensitivity was also reduced under dark-adapted conditions for both stimulus colors for the NDR (mean defect ± SEM = -1.9 dB ± 0.7) and MDR (mean defect ± SEM = -4.5 ± 1.0 dB) groups. For both diabetic groups, field loss tended to be diffuse under light-adapted conditions (up to 6.9 dB loss) and localized under dark-adapted conditions (up to 15.4 dB loss).

Conclusions

Visual field sensitivity losses suggest neural abnormalities in early stage diabetic eye disease and the pattern of the sensitivity losses differed depending on the adaptation conditions. Chromatic perimetry may be useful for subtyping individuals who have mild or no diabetic retinopathy and for better understanding their neural dysfunction.

Rhegmatogenous retinal detachment surgery: A review

Rhegmatogenous retinal detachment (RRD) is a serious surgical condition with significant ocular morbidity if not managed properly. Once untreatable, approaches to the repair of RRD have greatly evolved over the years, leading to outstanding primary surgical success rates. The management of RRD is often a topic of great debate. Scleral buckling, vitrectomy and pneumatic retinopexy have been used successfully for the treatment of RRD. Several factors may affect surgical success and dictate a surgeon's preference for the technique employed. In this review, we provide an overview and supporting literature on the options for RRD repair and their respective preoperative and postoperative considerations in order to guide surgical management.

Automated Region of Interest Selection Improves Deep Learning-Based Segmentation of Hyper-Reflective Foci in Optical Coherence Tomography Images

Hyperreflective foci (HRF) have been associated with retinal disease progression and demonstrated as a negative prognostic biomarker for visual function. Automated segmentation of HRF in retinal optical coherence tomography (OCT) scans can be beneficial to identify the formation and movement of the HRF biomarker as a retinal disease progresses and can serve as the first step in understanding the nature and severity of the disease. In this paper, we propose a fully automated deep neural network based HRF segmentation model in OCT images. We enhance the model's performance by using a patch-based strategy that increases the model's compute on the HRF pixels. The patch-based strategy is evaluated against state of the art HRF segmentation pipelines on clinical retinal image data. Our results shows that the patch-based approach demonstrates a high precision score and intersection over union (IOU) using a ResNet34 segmentation model with Binary Cross Entropy loss function. The HRF segmentation pipeline can be used for analyzing HRF biomarkers for different retinopathies.

MF-AV-Net: an open-source deep learning network with multimodal fusion options for artery-vein segmentation in OCT angiography

This study is to demonstrate the effect of multimodal fusion on the performance of deep learning artery-vein (AV) segmentation in optical coherence tomography (OCT) and OCT angiography (OCTA); and to explore OCT/OCTA characteristics used in the deep learning AV segmentation. We quantitatively evaluated multimodal architectures with early and late OCT-OCTA fusions, compared to the unimodal architectures with OCT-only and OCTA-only inputs. The OCTA-only architecture, early OCT-OCTA fusion architecture, and late OCT-OCTA fusion architecture yielded competitive performances. For the 6 mm×6 mm and 3 mm×3 mm datasets, the late fusion architecture achieved an overall accuracy of 96.02% and 94.00%, slightly better than the OCTA-only architecture which achieved an overall accuracy of 95.76% and 93.79%. 6 mm×6 mm OCTA images show AV information at pre-capillary level structure, while 3 mm×3 mm OCTA images reveal AV information at capillary level detail. In order to interpret the deep learning performance, saliency maps were produced to identify OCT/OCTA image characteristics for AV segmentation. Comparative OCT and OCTA saliency maps support the capillary-free zone as one of the possible features for AV segmentation in OCTA. The deep learning network MF-AV-Net used in this study is available on GitHub for open access.

Artificial Intelligence Detection of Diabetic Retinopathy: Subgroup Comparison of the EyeArt System with Ophthalmologists' Dilated Exams

Objective

To compare general ophthalmologists, retina specialists, and the EyeArt Artificial Intelligence (AI) system to the clinical reference standard for detecting more than mild diabetic retinopathy (mtmDR).

Design

Prospective, pivotal, multicenter trial conducted from April 2017 to May 2018.

Participants

Participants were aged ≥ 18 years who had diabetes mellitus and underwent dilated ophthalmoscopy. A total of 521 of 893 participants met these criteria and completed the study protocol.

Testing

Participants underwent 2-field fundus photography (macula centered, disc centered) for the EyeArt system, dilated ophthalmoscopy, and 4-widefield stereoscopic dilated fundus photography for reference standard grading.

Main Outcome Measures

For mtmDR detection, sensitivity and specificity of EyeArt gradings of 2-field, fundus photographs and ophthalmoscopy grading versus a rigorous clinical reference standard comprising Reading Center grading of 4-widefield stereoscopic dilated fundus photographs using the ETDRS severity scale. The AI system provided automatic eye-level results regarding mtmDR.

Results

Overall, 521 participants (999 eyes) at 10 centers underwent dilated ophthalmoscopy: 406 by nonretina and 115 by retina specialists. Reading Center graded 207 positive and 792 eyes negative for mtmDR. Of these 999 eyes, 26 eyes were ungradable by the EyeArt system, leaving 973 eyes with both EyeArt and Reading Center gradings. Retina specialists correctly identified 22 of 37 eyes as positive (sensitivity 59.5%) and 182 of 184 eyes as negative (specificity 98.9%) for mtmDR versus the EyeArt AI system that identified 36 of 37 as positive (sensitivity 97%) and 162 of 184 eyes as negative (specificity of 88%) for mtmDR. General ophthalmologists correctly identified 35 of 170 eyes as positive (sensitivity 20.6%) and 607 of 608 eyes as negative (specificity 99.8%) for mtmDR compared with the EyeArt AI system that identified 164 of 170 as positive (sensitivity 96.5%) and 525 of 608 eyes as negative (specificity 86%) for mtmDR.

Conclusions

The AI system had a higher sensitivity for detecting mtmDR than either general ophthalmologists or retina specialists compared with the clinical reference standard. It can potentially serve as a low-cost point-of-care diabetic retinopathy detection tool and help address the diabetic eye screening burden.

QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY REVEALS ROD PHOTORECEPTOR DEGENERATION in EARLY DIABETIC RETINOPATHY

PURPOSE

This study is to test the feasibility of optical coherence tomography (OCT) detection of photoreceptor abnormality and to verify that the photoreceptor abnormality is rod predominated in early diabetic retinopathy (DR).

METHODS

OCT images were acquired from normal eyes, diabetic eyes with no DR, and mild nonproliferative DR (NPDR). Quantitative features, including thickness measurements quantifying band distances and reflectance intensity features among the external limiting membrane, inner segment ellipsoid, interdigitation zone, and retinal pigment epithelium were determined. Comparative OCT analysis of central fovea, parafovea, and perifovea were implemented to verify that the photoreceptor abnormality is rod predominated in early DR.

RESULTS

Thickness abnormalities between the inner segment ellipsoid and interdigitation zone also showed a decreasing trend among cohorts. Reflectance abnormalities of the external limiting membrane, interdigitation zone, and inner segment ellipsoid were observed between healthy, no DR, and mild NPDR eyes. The normalized inner segment ellipsoid/retinal pigment epithelium intensity ratio revealed a significant decreasing trend in the perifovea, but no detectable difference in central fovea.

CONCLUSION

Quantitative OCT analysis consistently revealed outer retina, i.e., photoreceptor changes in diabetic patients with no DR and mild NPDR. Comparative analysis of central fovea, parafovea, and perifovea confirmed that the photoreceptor abnormality is rod-predominated in early DR.

From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration

OBJECTIVE

Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD.

PURPOSE

To delineate the state of AI for AMD, including current data, standards, achievements, and challenges.

METHODS

Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus.

RESULTS

Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization.

CONCLUSIONS

Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.

Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

Importance

Diabetic retinopathy (DR) is a leading cause of blindness in adults worldwide. Early detection and intervention can prevent blindness; however, many patients do not receive their recommended annual diabetic eye examinations, primarily owing to limited access.

Objective

To evaluate the safety and accuracy of an artificial intelligence (AI) system (the EyeArt Automated DR Detection System, version 2.1.0) in detecting both more-than-mild diabetic retinopathy (mtmDR) and vision-threatening diabetic retinopathy (vtDR).

Design, Setting, and Participants

A prospective multicenter cross-sectional diagnostic study was preregistered (NCT03112005) and conducted from April 17, 2017, to May 30, 2018. A total of 942 individuals aged 18 years or older who had diabetes gave consent to participate at 15 primary care and eye care facilities. Data analysis was performed from February 14 to July 10, 2019.

Interventions

Retinal imaging for the autonomous AI system and Early Treatment Diabetic Retinopathy Study (ETDRS) reference standard determination.

Main Outcomes and Measures

Primary outcome measures included the sensitivity and specificity of the AI system in identifying participants' eyes with mtmDR and/or vtDR by 2-field undilated fundus photography vs a rigorous clinical reference standard comprising reading center grading of 4 wide-field dilated images using the ETDRS severity scale. Secondary outcome measures included the evaluation of imageability, dilated-if-needed analysis, enrichment correction analysis, worst-case imputation, and safety outcomes.

Results

Of 942 consenting individuals, 893 patients (1786 eyes) met the inclusion criteria and completed the study protocol. The population included 449 men (50.3%). Mean (SD) participant age was 53.9 (15.2) years (median, 56; range, 18-88 years), 655 were White (73.3%), and 206 had type 1 diabetes (23.1%). Sensitivity and specificity of the AI system were high in detecting mtmDR (sensitivity: 95.5%; 95% CI, 92.4%-98.5% and specificity: 85.0%; 95% CI, 82.6%-87.4%) and vtDR (sensitivity: 95.1%; 95% CI, 90.1%-100% and specificity: 89.0%; 95% CI, 87.0%-91.1%) without dilation. Imageability was high without dilation, with the AI system able to grade 87.4% (95% CI, 85.2%-89.6%) of the eyes with reading center grades. When eyes with ungradable results were dilated per the protocol, the imageability improved to 97.4% (95% CI, 96.4%-98.5%), with the sensitivity and specificity being similar. After correcting for enrichment, the mtmDR specificity increased to 87.8% (95% CI, 86.3%-89.5%) and the sensitivity remained similar; for vtDR, both sensitivity (97.0%; 95% CI, 91.2%-100%) and specificity (90.1%; 95% CI, 89.4%-91.5%) improved.

Conclusions and Relevance

This prospective multicenter cross-sectional diagnostic study noted safety and accuracy with use of the EyeArt Automated DR Detection System in detecting both mtmDR and, for the first time, vtDR, without physician assistance. These findings suggest that improved access to accurate, reliable diabetic eye examinations may increase adherence to recommended annual screenings and allow for accelerated referral of patients identified as having vtDR.

Imaging and artificial intelligence for progression of age-related macular degeneration

Age-related macular degeneration (AMD) is a leading cause of severe vision loss. With our aging population, it may affect 288 million people globally by the year 2040. AMD progresses from an early and intermediate dry form to an advanced one, which manifests as choroidal neovascularization and geographic atrophy. Conversion to AMD-related exudation is known as progression to neovascular AMD, and presence of geographic atrophy is known as progression to advanced dry AMD. AMD progression predictions could enable timely monitoring, earlier detection and treatment, improving vision outcomes. Machine learning approaches, a subset of artificial intelligence applications, applied on imaging data are showing promising results in predicting progression. Extracted biomarkers, specifically from optical coherence tomography scans, are informative in predicting progression events. The purpose of this mini review is to provide an overview about current machine learning applications in artificial intelligence for predicting AMD progression, and describe the various methods, data-input types, and imaging modalities used to identify high-risk patients. With advances in computational capabilities, artificial intelligence applications are likely to transform patient care and management in AMD. External validation studies that improve generalizability to populations and devices, as well as evaluating systems in real-world clinical settings are needed to improve the clinical translations of artificial intelligence AMD applications.

Probabilistic Forecasting of Anti-VEGF Treatment Frequency in Neovascular Age-Related Macular Degeneration

Purpose

To probabilistically forecast needed anti-vascular endothelial growth factor (anti-VEGF) treatment frequency using volumetric spectral domain-optical coherence tomography (SD-OCT) biomarkers in neovascular age-related macular degeneration from real-world settings.

Methods

SD-OCT volume scans were segmented with a custom deep-learning-based analysis pipeline. Retinal thickness and reflectivity values were extracted for the central and the four inner Early Treatment Diabetic Retinopathy Study (ETDRS) subfields for six retinal layers (inner retina, outer nuclear layer, inner segments [IS], outer segments [OS], retinal pigment epithelium-drusen complex [RPEDC] and the choroid). Machine-learning models were probed to predict the anti-VEGF treatment frequency within the next 12 months. Probabilistic forecasting was performed using natural gradient boosting (NGBoost), which outputs a full probability distribution. The mean absolute error (MAE) between the predicted versus actual anti-VEGF treatment frequency was the primary outcome measure.

Results

In a total of 138 visits of 99 eyes with neovascular AMD (96 patients) from two clinical centers, the prediction of future anti-VEGF treatment frequency was observed with an accuracy (MAE [95% confidence interval]) of 2.60 injections/year [2.25-2.96] (R2 = 0.390) using random forest regression and 2.66 injections/year [2.31-3.01] (R2 = 0.094) using NGBoost, respectively. Prediction intervals were well calibrated and reflected the true uncertainty of NGBoost-based predictions. Standard deviation of RPEDC-thickness in the central ETDRS-subfield constituted an important predictor across models.

Conclusions

The proposed, fully automated pipeline enables probabilistic forecasting of future anti-VEGF treatment frequency in real-world settings.

Translational Relevance

Prediction of a probability distribution allows the physician to inspect the underlying uncertainty. Predictive uncertainty estimates are essential to highlight cases where human-inspection and/or reversion to a fallback alternative is warranted.

Longitudinal Assessment of Retinal Thinning in Adults With and Without Sickle Cell Retinopathy Using Spectral-Domain Optical Coherence Tomography

Importance

Determination of retinal thinning rates may help to identify patients who are at risk of progression of sickle cell retinopathy.

Objective

To assess the rates of macular thinning in adults with and without sickle cell retinopathy using spectral-domain optical coherence tomography (OCT) and to identify ocular and systemic risk factors associated with retinal thinning.

Design, Setting, and Participants

This longitudinal prospective case-control study enrolled adult participants from a university-based retina subspecialty clinic between February 11, 2009, and July 3, 2019. The study was designed in autumn 2008 and conducted from February 2, 2009, to July 3, 2020. Participants with sickle cell retinopathy (sickle cell group) were matched by age and race with participants without sickle cell retinopathy (control group). Participants received annual spectral-domain OCT and clinical examinations. Those with at least 1 year of follow-up by July 3, 2020, were included in the analysis. Data were analyzed from February 2, 2009, to July 3, 2020.

Main Outcomes and Measures

The primary outcome was comparison of spectral-domain OCT measurements from early-treatment diabetic retinopathy study subfield rates of retinal thinning between eyes with and without sickle cell retinopathy and between different sickle cell hemoglobin subtypes. The secondary outcome was identification of ocular and systemic risk factors associated with rates of retinal thinning.

Results

Among 370 adults (711 eyes) enrolled in the study, 310 participants (606 eyes) had sickle cell retinopathy, and 60 participants (105 eyes) did not. Of those, 175 of 310 participants (56.5%; 344 of 606 eyes [56.8%]; mean [SD] age, 37.8 [12.8] years; 126 women [72.0%]) in the sickle cell group and 31 of 60 participants (51.7%; 46 of 105 eyes [43.8%]; mean [SD] age, 59 [15.4] years; 22 women [71.0%]) in the control group had at least 1 year of clinical and spectral-domain OCT follow-up data from baseline. The mean (SD) follow-up was 53.7 (32.6) months for the sickle cell group and 54.6 (34.9) months for the control group. Rates of macular thinning in the sickle cell group were significantly higher than those in the control group for the inner nasal (difference, -1.18 μm per year; 95% CI, -1.71 to -0.65 μm per year), inner superior (difference, -1.03 μm per year; 95% CI, -1.78 to -0.29 μm per year), inner temporal (difference, -0.61 μm per year; 95% CI, -1.16 to -0.07 μm per year), and outer nasal (difference, -0.41 μm per year; 95% CI, -0.80 to -0.03 μm per year) quadrants. Patients with sickle cell hemoglobin SC and sickle cell hemoglobin β-thalassemia subtypes had higher rates of retinal thinning than those with the sickle cell hemoglobin SS subtype. Risk factors associated with greater rates of retinal thinning included participant age, stage of retinopathy, previous stroke, and presence of hypertension, acute chest syndrome, or diabetes. Hydroxyurea therapy was associated with decreased rates of retinal thinning and may be a protective factor.

Conclusions and Relevance

In this study, rates of retinal thinning were higher among participants with sickle cell retinopathy compared with those without sickle cell retinopathy, and thinning rates increased with participant age and stage of retinopathy. These findings suggest that identifying anatomic worsening of sickle cell maculopathy through spectral-domain OCT may be a useful parameter to evaluate the progression of sickle cell retinopathy.

VASCULAR COMPLEXITY ANALYSIS IN OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY OF DIABETIC RETINOPATHY

PURPOSE

This study aimed to verify the feasibility of using vascular complexity features for objective differentiation of controls and nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) patients.

METHODS

This was a cross-sectional study conducted in a tertiary, subspecialty, academic practice. The cohort included 20 control subjects, 60 NPDR patients, and 56 PDR patients. Three vascular complexity features, including the vessel complexity index, fractal dimension, and blood vessel tortuosity, were derived from each optical coherence tomography angiography image. A shifting-window measurement was further implemented to identify local feature distortions due to localized neovascularization and mesh structures in PDR.

RESULTS

With mean value analysis of the whole-image, only the vessel complexity index and blood vessel tortuosity were able to classify NPDR versus PDR patients. Comparative shifting-window measurement revealed increased sensitivity of complexity feature analysis, particularly for NPDR versus PDR classification. A multivariate regression model indicated that the combination of all three vascular complexity features with shifting-window measurement provided the best classification accuracy for controls versus NPDR versus PDR.

CONCLUSION

Vessel complexity index and blood vessel tortuosity were the most sensitive in differentiating NPDR and PDR patients. A shifting-window measurement increased the sensitivity significantly for objective optical coherence tomography angiography classification of diabetic retinopathy.

AMPLITUDE LOSS OF THE HIGH-FREQUENCY FLICKER ELECTRORETINOGRAM IN EARLY DIABETIC RETINOPATHY

PURPOSE

To evaluate retinal dysfunction in diabetic patients who have mild or no nonproliferative diabetic retinopathy (DR) using the high-frequency flicker electroretinogram.

METHODS

Light-adapted flicker electroretinograms were recorded from 15 diabetic patients who have no clinically apparent retinopathy, 15 diabetic patients who have mild nonproliferative DR, and 15 nondiabetic, age-equivalent controls. Electroretinograms were elicited by full-field flicker at 2 temporal frequencies, 31.25 and 62.5 Hz, and were recorded using conventional techniques. Amplitude and timing of the flicker responses were compared among the groups and correlated with clinical characteristics including age, acuity, disease duration, and HbA1c.

RESULTS

The 31.25-Hz flicker amplitude was slightly, but nonsignificantly, smaller for subjects with no DR and mild nonproliferative DR , compared with the control group (both t < 1.38, P > 0.31); small, nonsignificant response delays for both patient groups were also observed (both t < 1.57, P > 0.12). By contrast, there were significant amplitude reductions for the 62.5-Hz flicker stimulus: mean amplitude was reduced by 32% for subjects with no DR and by 41% for subjects with mild nonproliferative DR (both t > 2.92 and P < 0.01). Response timing at 62.5 Hz did not differ significantly from control for either group (both t < 1.2 and P > 0.39). Electroretinogram amplitude and timing were not correlated significantly with clinical characteristics.

CONCLUSION

The 62.5-Hz flicker electroretinogram is useful for evaluating retinal dysfunction in diabetic patients who have mild or no DR because this response can be significantly reduced. Attenuation of the high-frequency flicker electroretinogram, which is primarily generated by bipolar cells, suggests a relatively early retinal site of neural dysfunction.

AV-Net: deep learning for fully automated artery-vein classification in optical coherence tomography angiography

This study is to demonstrate deep learning for automated artery-vein (AV) classification in optical coherence tomography angiography (OCTA). The AV-Net, a fully convolutional network (FCN) based on modified U-shaped CNN architecture, incorporates enface OCT and OCTA to differentiate arteries and veins. For the multi-modal training process, the enface OCT works as a near infrared fundus image to provide vessel intensity profiles, and the OCTA contains blood flow strength and vessel geometry features. A transfer learning process is also integrated to compensate for the limitation of available dataset size of OCTA, which is a relatively new imaging modality. By providing an average accuracy of 86.75%, the AV-Net promises a fully automated platform to foster clinical deployment of differential AV analysis in OCTA.

Transfer Learning for Automated OCTA Detection of Diabetic Retinopathy

Purpose

To test the feasibility of using deep learning for optical coherence tomography angiography (OCTA) detection of diabetic retinopathy.

Methods

A deep-learning convolutional neural network (CNN) architecture, VGG16, was employed for this study. A transfer learning process was implemented to retrain the CNN for robust OCTA classification. One dataset, consisting of images of 32 healthy eyes, 75 eyes with diabetic retinopathy (DR), and 24 eyes with diabetes but no DR (NoDR), was used for training and cross-validation. A second dataset consisting of 20 NoDR and 26 DR eyes was used for external validation. To demonstrate the feasibility of using artificial intelligence (AI) screening of DR in clinical environments, the CNN was incorporated into a graphical user interface (GUI) platform.

Results

With the last nine layers retrained, the CNN architecture achieved the best performance for automated OCTA classification. The cross-validation accuracy of the retrained classifier for differentiating among healthy, NoDR, and DR eyes was 87.27%, with 83.76% sensitivity and 90.82% specificity. The AUC metrics for binary classification of healthy, NoDR, and DR eyes were 0.97, 0.98, and 0.97, respectively. The GUI platform enabled easy validation of the method for AI screening of DR in a clinical environment.

Conclusions

With a transfer learning process for retraining, a CNN can be used for robust OCTA classification of healthy, NoDR, and DR eyes. The AI-based OCTA classification platform may provide a practical solution to reducing the burden of experienced ophthalmologists with regard to mass screening of DR patients.

Translational Relevance

Deep-learning-based OCTA classification can alleviate the need for manual graders and improve DR screening efficiency.

Contrast sensitivity is associated with outer-retina thickness in early-stage diabetic retinopathy

PURPOSE

To determine the relationship between contrast sensitivity (CS) and outer-retina thickness (ORT) in diabetics who have minimal or no diabetic retinopathy (DR).

METHODS

Twenty non-diabetic control subjects and 40 type-2 diabetic subjects participated (20 had no clinically apparent DR [NDR] and 20 had mild non-proliferative DR [NPDR]). No subject had a history of treatment for macular oedema. Letter CS, microperimetry (MP) sensitivity and visual acuity (VA) were measured. Letter CS and MP measurements were performed over the central 6° of the visual field. Spectral domain optical coherence tomography (SD-OCT) images were obtained at corresponding locations, outer-retina thickness was quantified, and structure-function relationships were evaluated.

RESULTS

Analysis of variance indicated significant letter CS differences among the groups (p < 0.001). Letter CS was reduced significantly for the mild NPDR group (p < 0.001; 33% reduction), but not the NDR group (p = 0.08). There were no significant differences in MP sensitivity or ORT among the groups (both p > 0.10). Nevertheless, Hoeffding's D tests indicated significant associations between ORT and letter CS (p < 0.001) and between ORT and MP sensitivity for the mild NPDR group (p = 0.01). VA was not significantly associated with ORT for either diabetic group (both p > 0.49).

CONCLUSIONS

Outer-retina thickness is associated with letter CS and MP sensitivity, but not VA, in mild NPDR. This finding highlights the usefulness of simple letter CS measures and suggests neural dysfunction can occur in the absence of marked structural abnormalities in early-stage DR.

ADVERSE EVENTS OF THE ARGUS II RETINAL PROSTHESIS: Incidence, Causes, and Best Practices for Managing and Preventing Conjunctival Erosion

PURPOSE

To analyze and provide an overview of the incidence, management, and prevention of conjunctival erosion in Argus II clinical trial subjects and postapproval patients.

METHODS

This retrospective analysis followed the results of 274 patients treated with the Argus II Retinal Prosthesis System between June 2007 and November 2017, including 30 subjects from the US and European clinical trials, and 244 patients in the postapproval phase. Results were gathered for incidence of a serious adverse event, incidence of conjunctival erosion, occurrence sites, rates of erosion, and erosion timing.

RESULTS

Overall, 60% of subjects in the clinical trial subjects versus 83% of patients in the postapproval phase did not experience device- or surgery-related serious adverse events. In the postapproval phase, conjunctival erosion had an incidence rate of 6.2% over 5 years and 11 months. In 55% of conjunctival erosion cases, erosion occurred in the inferotemporal quadrant, 25% in the superotemporal quadrant, and 20% in both. Sixty percent of the erosion events occurred in the first 15 months after implantation, and 85% within the first 2.5 years.

CONCLUSION

Reducing occurrence of conjunctival erosion in patients with the Argus II Retinal Prosthesis requires identification and minimization of risk factors before and during implantation. Implementing inverted sutures at the implant tabs, use of graft material at these locations as well as Mersilene rather than nylon sutures, and accurate Tenon's and conjunctiva closure are recommended for consideration in all patients.

QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY

PURPOSE

This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging.

METHODS

One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity, and accuracy were used as performance metrics of computer-aided classification, and receiver operation characteristics curve was plotted to measure the sensitivity-specificity tradeoff of the classification algorithm.

RESULTS

Among 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus mild NPDR, respectively. Combined feature classification achieved improved accuracies, 94.41% and 92.96%, respectively. Moreover, the temporal-perifoveal region was the most sensitive region for early detection of DR. For multiclass classification, support vector machine algorithm achieved 84% accuracy.

CONCLUSION

Blood vessel density was observed as the most sensitive feature, and temporal-perifoveal region was the most sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR.

Simulating vascular leakage on optical coherence tomography angiography using an overlay technique with corresponding thickness maps

BACKGROUND

To demonstrate a technique for using optical coherence tomography angiography (OCTA) to simulate leakage in eyes with diabetic macular oedema and determine the sensitivity and positive predictive value of detecting leaking microvasculature on OCTA using fluorescein angiography (FA) as the comparative norm.

METHODS

6×6 mm OCT angiograms were overlaid with the corresponding OCT thickness maps. Microvascular abnormalities on the OCT angiogram underlying areas of thickening on the OCT thickness map were assumed to be leaking. Two independent readers blindly read the OCTA overlay images then the FA images cropped to the same approximate region to delineate areas of leaking microvasculature. The results were compared to determine the sensitivity and positive predictive value of OCTA for detection of leaking vessels.

RESULTS

28 eyes of 19 diabetic patients were included. Each eye demonstrated an average of seven leaking microvascular abnormalities on the OCTA images and 22 leaking abnormalities on the FA images. Sensitivity of leaking microvasculature detection by OCTA was 26.1% and positive predictive value was 68.4%. The correlation coefficient of the two readers' detection of leaking microvasculature was 0.605 for OCTA reads compared with 0.916 for FA.

CONCLUSION

OCTA as a whole can be used to simulate leakage, but currently, sensitivity of the technique is low. Further understanding of the OCTA technology may yield novel means of detecting retinal pathology.

Longitudinal Study of Peripapillary Thinning in Sickle Cell Hemoglobinopathies

PURPOSE

To determine the rate of retinal nerve fiber layer (RNFL) thinning in patients with sickle cell hemoglobinopathies.

DESIGN

This was a prospective cohort study.

METHODS

Sixty-seven patients averaging 35.8 ± 11.5 years of age at enrollment with electrophoretically confirmed sickle cell hemoglobinopathies followed by the University of Illinois at Chicago retina clinic for ≥1 year were included. Exclusion criteria included a history of diabetes, uncontrolled hypertension, glaucoma, ocular opacities, other retinopathies, and previous retinal procedures. The optic nerve head RNFL thicknesses were measured with spectral-domain optical coherence tomography (Heidelberg Engineering, Inc) at enrollment and subsequent follow-ups. Linear mixed models were used to estimate rates of thinning.

RESULTS

A total of 122 eyes were followed for 3.8 ± 2.0 years (range 1-8 years). Mean global peripapillary RNFL thickness was 100.9 ± 13.0 μm at baseline. Global peripapillary RNFL thickness decreased at a rate of 0.98 μm per year (95% confidence interval [CI] 0.77-1.19 μm/year). A history of stroke was associated with a faster rate of global RNFL thinning (1.72 ± 0.20 vs 0.79 ± 0.12 μm/year, P < .001), whereas a history of hypertension was associated with a slower rate of thinning (0.33 ± 0.27 vs 1.14 ± 0.12 μm/year, P = .002).

CONCLUSIONS

Peripapillary RNFL thinning in patients with sickle cell hemoglobinopathies occurred faster in patients with a history of stroke and slower in patients with controlled hypertension. Future studies will compare these rates to those of healthy age- and race-matched individuals.

Color Fundus Image Guided Artery-Vein Differentiation in Optical Coherence Tomography Angiography

Purpose

This study aimed to develop a method for automated artery-vein classification in optical coherence tomography angiography (OCTA), and to verify that differential artery-vein analysis can improve the sensitivity of OCTA detection and staging of diabetic retinopathy (DR).

Methods

For each patient, the color fundus image was used to guide the artery-vein differentiation in the OCTA image. Traditional mean blood vessel caliber (m-BVC) and mean blood vessel tortuosity (m-BVT) in OCTA images were quantified for control and DR groups. Artery BVC (a-BVC), vein BVC (v-BVC), artery BVT (a-BVT), and vein BVT (a-BVT) were calculated, and then the artery-vein ratio (AVR) of BVC (AVR-BVC) and AVR of BVT (AVR-BVT) were quantified for comparative analysis. Sensitivity, specificity, and accuracy were used as performance metrics of artery-vein classification. One-way, multilabel ANOVA with Bonferroni's test and Student's t-test were employed for statistical analysis.

Results

Forty eyes of 20 control subjects and 80 eyes of 48 NPDR patients (18 mild, 16 moderate, and 14 severe NPDR) were evaluated in this study. The color fundus image-guided artery-vein differentiation reliably identified individual arteries and veins in OCTA. AVR-BVC and AVR-BVT provided significant (P < 0.001) and moderate (P < 0.05) improvements, respectively, in detecting and classifying NPDR stages, compared with traditional m-BVC analysis.

Conclusions

Color fundus image-guided artery-vein classification provides a feasible method to differentiate arteries and veins in OCTA. Differential artery-vein analysis can improve the sensitivity of OCTA detection and classification of DR. AVR-BVC is the most-sensitive feature, which can classify control and mild NPDR, providing a quantitative biomarker for objective detection of early DR.

Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy

This study is to establish quantitative features of vascular geometry in optical coherence tomography angiography (OCTA) and validate them for the objective classification of diabetic retinopathy (DR). Six geometric features, including total vessel branching angle (VBA: θ), child branching angles (CBAs: α1 and α2), vessel branching coefficient (VBC), and children-to-parent vessel width ratios (VWR1 and VWR2), were automatically derived from each vessel branch in OCTA. Comparative analysis of heathy control, diabetes with no DR (NoDR), and non-proliferative DR (NPDR) was conducted. Our study reveals four quantitative OCTA features to produce robust DR detection and staging classification: (ANOVA, P<0.05), VBA, CBA1, VBC, and VWR1.

Electrophysiological and pupillometric measures of inner retina function in nonproliferative diabetic retinopathy

PURPOSE

To evaluate three measures of inner retina function, the pattern electroretinogram (pERG), the photopic negative response (PhNR), and the post-illumination pupil response (PIPR) in diabetics with and without nonproliferative diabetic retinopathy (NPDR).

METHODS

Fifteen non-diabetic control subjects and 45 type 2 diabetic subjects participated (15 have no clinically apparent retinopathy [NDR], 15 have mild NPDR, and 15 have moderate/severe NPDR). The pERG was elicited by a contrast-reversing checkerboard pattern, and the PhNR was measured in response to a full-field, long-wavelength flash presented against a short-wavelength adapting field. The PIPR was elicited by a full-field, 450 cd/m², short-wavelength flash. All responses were recorded and analyzed using conventional techniques. One-way ANOVAs were performed to compare the pERG, PhNR, and PIPR among the control and diabetic groups.

RESULTS

ANOVA indicated statistically significant differences among the control and diabetic subjects for all three measures. Holm-Sidak post hoc comparisons indicated small, nonsignificant reductions in the pERG (8%), PhNR (8%), and PIPR (10%) for the NDR group compared to the controls (all p > 0.25). In contrast, there were significant reductions in the pERG (35), PhNR (34%), and PIPR (30%) for the mild NPDR group compared to the controls (all p < 0.01). Likewise, there were significant reductions in the pERG (40%), PhNR (32%), and PIPR (32%) for the moderate/severe NPDR group compared to the controls (all p < 0.01).

CONCLUSION

Abnormalities of the pERG, PhNR, and PIPR suggest inner retina neural dysfunction in diabetics who have clinically apparent vascular abnormalities. Taken together, these measures provide a noninvasive, objective approach to study neural dysfunction in these individuals.

Outcomes of 25-gauge vitrectomy with relaxing retinectomy for retinal detachment secondary to proliferative vitreoretinopathy

Purpose

The aim of this study is to evaluate visual and anatomic outcomes of 25-gauge vitrectomy with relaxing retinectomies for complex retinal detachment (RD) secondary to proliferative vitreoretinopathy (PVR).

Methods

A single-center, retrospective case series of 44 patients who had undergone a 25-gauge vitrectomy with a relaxing retinectomy for the treatment of combined RD and PVR was performed. Pre-operative characteristics, intraoperative techniques, and outcomes were analyzed. The rates of attachment, complications, and visual acuity were analyzed. Institutional Review Board/Ethics Committee approval was obtained and the described research adhered to the tenets of the Declaration of Helsinki.

Results

At the final follow-up, 27 eyes (61%) had attachment after one surgery, 41 eyes (93%) ultimately had attached retinas, 3 eyes (7%) had hypotony, 3 eyes had become phthisical (7%), and 24 eyes (56%) had improved visual acuity. After stratifying by visual outcomes, 20/400 or better BCVA was not associated with age (p=0.66), RD etiology (p=0.61), pre-operative hypotony (p=0.60), nor size of retinectomy (p=0.48). Patients achieving 20/400 vision or better were statistically more likely to be pseudophakic (p=0.024) and have silicone oil removal (p<0.0001).

Conclusions

The use of 25-gauge vitrectomy and relaxing retinectomy provides a high rate of reattachment and improved visual acuity.

OCT feature analysis guided artery-vein differentiation in OCTA

Differential artery-vein analysis promises better sensitivity for retinal disease detection and classification. However, clinical optical coherence tomography angiography (OCTA) instruments lack the function of artery-vein differentiation. This study aims to verify the feasibility of using OCT intensity feature analysis to guide artery-vein differentiation in OCTA. Four OCT intensity profile features, including i) ratio of vessel width to central reflex, ii) average of maximum profile brightness, iii) average of median profile intensity, and iv) optical density of vessel boundary intensity compared to background intensity, are used to classify artery-vein source nodes in OCT. A blood vessel tracking algorithm is then employed to automatically generate the OCT artery-vein map. Given the fact that OCT and OCTA are intrinsically reconstructed from the same raw spectrogram, the OCT artery-vein map is able to guide artery-vein differentiation in OCTA directly.

Differential Artery-Vein Analysis Improves the Performance of OCTA Staging of Sickle Cell Retinopathy

Purpose

We test if differential artery–vein analysis can increase the performance of optical coherence tomography angiography (OCTA) detection and classification of sickle cell retinopathy (SCR).

Method

This observational case series was conducted in a tertiary-retina practice. Color fundus and OCTA images were collected from 20 control and 48 SCR subjects. Fundus data were collected from fundus imaging devices, and SD-OCT and corresponding OCTA data were acquired using a spectral-domain OCT (SD-OCT) angiography system. For each patient, color fundus image-guided artery–vein classification was conducted in the OCTA image. Traditional mean blood vessel tortuosity (m-BVT) and mean blood vessel caliber (m-BVC) in OCTA images were quantified for control and SCR groups. Artery BVC (a-BVC), vein BVC (v-BVC), artery BVT (a-BVT), and vein BVT (v-BVT) were calculated; and then the artery–vein ratio of BVC (AVR–BVC) and artery–vein ratio of BVT (AVR–BVT) were quantified for comparative analysis.

Results

We evaluated 40 control and 85 SCR images in this study. The color fundus image-guided artery–vein classification had 97.02% accuracy for differentiating arteries and veins in OCTA. Differential artery–vein analysis provided significant improvement (P < 0.05) in detecting and classifying SCR stages compared to traditional mean blood vessel analysis. AVR–BVT and AVR–BVC showed significant (P < 0.001) correlation with SCR severity.

Conclusions

Differential artery–vein analysis can significantly improve the performance of OCTA detection and classification of SCR. AVR–BVT is the most sensitive feature that can classify control and mild SCR.

Translational Relevance

SCR and other retinovascular diseases result in changes to the caliber and tortuosity appearance of arteries and veins separately. Differential artery–vein analysis can improve the performance of SCR detection and stage classification.