Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop.

The accessibility of the retina with the use of non-invasive and relatively low-cost ophthalmic imaging techniques and analytics provides a unique opportunity to improve the detection, diagnosis and monitoring of systemic diseases. The National Heart, Lung, and Blood Institute conducted a workshop in October 2022 to examine this concept. On the basis of the discussions at that workshop, this Roadmap describes current knowledge gaps and new research opportunities to evaluate the relationships between the eye (in particular, retinal biomarkers) and the risk of cardiovascular diseases, including coronary artery disease, heart failure, stroke, hypertension and vascular dementia. Identified gaps include the need to simplify and standardize the capture of high-quality images of the eye by non-ophthalmic health workers and to conduct longitudinal studies using multidisciplinary networks of diverse at-risk populations with improved implementation and methods to protect participant and dataset privacy. Other gaps include improving the measurement of structural and functional retinal biomarkers, determining the relationship between microvascular and macrovascular risk factors, improving multimodal imaging 'pipelines', and integrating advanced imaging with 'omics', lifestyle factors, primary care data and radiological reports, by using artificial intelligence technology to improve the identification of individual-level risk. Future research on retinal microvascular disease and retinal biomarkers might additionally provide insights into the temporal development of microvascular disease across other systemic vascular beds.

Contributions of Brain Microstructures and Metabolism to Visual Field Loss Patterns in Glaucoma Using Archetypal and Information Gain Analyses.

Purpose: To investigate the contributions of the microstructural and metabolic brain environment to glaucoma and their association with visual field (VF) loss patterns by using advanced diffusion magnetic resonance imaging (dMRI), proton magnetic resonance spectroscopy (MRS), and clinical ophthalmic measures. Methods: Sixty-nine glaucoma and healthy subjects underwent dMRI and/or MRS at 3 Tesla. Ophthalmic data were collected from VF perimetry and optical coherence tomography. dMRI parameters of microstructural integrity in the optic radiation and MRS-derived neurochemical levels in the visual cortex were compared among early glaucoma, advanced glaucoma, and healthy controls. Multivariate regression was used to correlate neuroimaging metrics with 16 archetypal VF loss patterns. We also ranked neuroimaging, ophthalmic, and demographic attributes in terms of their information gain to determine their importance to glaucoma. Results: In dMRI, decreasing fractional anisotropy, radial kurtosis, and tortuosity and increasing radial diffusivity correlated with greater overall VF loss bilaterally. Regionally, decreasing intra-axonal space and extra-axonal space diffusivities correlated with greater VF loss in the superior-altitudinal area of the right eye and the inferior-altitudinal area of the left eye. In MRS, both early and advanced glaucoma patients had lower gamma-aminobutyric acid (GABA), glutamate, and choline levels than healthy controls. GABA appeared to associate more with superonasal VF loss, and glutamate and choline more with inferior VF loss. Choline ranked third for importance to early glaucoma, whereas radial kurtosis and GABA ranked fourth and fifth for advanced glaucoma. Conclusions: Our findings highlight the importance of non-invasive neuroimaging biomarkers and analytical modeling for unveiling glaucomatous neurodegeneration and how they reflect complementary VF loss patterns.

Deep-Learning-Based Group Pointwise Spatial Mapping of Structure to Function in Glaucoma.

Purpose: To establish generalizable pointwise spatial relationship between structure and function through occlusion analysis of a deep-learning (DL) model for predicting the visual field (VF) sensitivities from 3-dimensional (3D) OCT scan. Design: Retrospective cross-sectional study. Participants: A total of 2151 eyes from 1129 patients. Methods: A DL model was trained to predict 52 VF sensitivities of 24-2 standard automated perimetry from 3D spectral-domain OCT images of the optic nerve head (ONH) with 12 915 OCT-VF pairs. Using occlusion analysis, the contribution of each individual cube covering a 240 × 240 × 31.25 µm region of the ONH to the model's prediction was systematically evaluated for each OCT-VF pair in a separate test set that consisted of 996 OCT-VF pairs. After simple translation (shifting in x- and y-axes to match the ONH center), group t-statistic maps were derived to visualize statistically significant ONH regions for each VF test point within a group. This analysis allowed for understanding the importance of each super voxel (240 × 240 × 31.25 µm covering the entire 4.32 × 4.32 × 1.125 mm ONH cube) in predicting VF test points for specific patient groups. Main Outcome Measures: The region at the ONH corresponding to each VF test point and the effect of the former on the latter. Results: The test set was divided to 2 groups, the healthy-to-early-glaucoma group (792 OCT-VF pairs, VF mean deviation [MD]: -1.32 ± 1.90 decibels [dB]) and the moderate-to-advanced-glaucoma group (204 OCT-VF pairs, VF MD: -17.93 ± 7.68 dB). Two-dimensional group t-statistic maps (x, y projection) were generated for both groups, assigning related ONH regions to visual field test points. The identified influential structural locations for VF sensitivity prediction at each test point aligned well with existing knowledge and understanding of structure-function spatial relationships. Conclusions: This study successfully visualized the global trend of point-by-point spatial relationships between OCT-based structure and VF-based function without the need for prior knowledge or segmentation of OCTs. The revealed spatial correlations were consistent with previously published mappings. This presents possibilities of learning from trained machine learning models without applying any prior knowledge, potentially robust, and free from bias. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A Hybrid Deep Learning Classification of Perimetric Glaucoma Using Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from Three Anatomy Regions.

Précis: A hybrid deep-learning model combines NFL reflectance and other OCT parameters to improve glaucoma diagnosis. Objective: To investigate if a deep learning model could be used combine nerve fiber layer (NFL) reflectance and other OCT parameters for glaucoma diagnosis. Patients and Methods: This is a prospective observational study where of 106 normal subjects and 164 perimetric glaucoma (PG) patients. Peripapillary NFL reflectance map, NFL thickness map, optic head analysis of disc, and macular ganglion cell complex thickness were obtained using spectral domain OCT. A hybrid deep learning model combined a fully connected network (FCN) and a convolution neural network (CNN) to develop to combine those OCT maps and parameters to distinguish normal and PG eyes. Two deep learning models were compared based on whether the NFL reflectance map was used as part of the input or not. Results: The hybrid deep learning model with reflectance achieved 0.909 sensitivity at 99% specificity and 0.926 at 95%. The overall accuracy was 0.948 with 0.893 sensitivity and 1.000 specificity, and the AROC was 0.979, which is significantly better than the logistic regression models (p < 0.001). The second best model is the hybrid deep learning model w/o reflectance, which also had significantly higher AROC than logistic regression models (p < 0.001). Logistic regression with reflectance model had slightly higher AROC or sensitivity than the other logistic regression model without reflectance (p = 0.024). Conclusions: Hybrid deep learning model significantly improved the diagnostic accuracy, without or without NFL reflectance. Hybrid deep learning model, combining reflectance/NFL thickness/GCC thickness/ONH parameter, may be a practical model for glaucoma screen purposes.

Disparities in Visual Field Testing Frequency Among Subjects With Glaucoma.

Purpose: Prior evidence suggests racial disparities in the utilization of visual field testing (VFT) for the diagnosis and monitoring of glaucoma. In this study, we considered the effect of baseline glaucoma severity and socioeconomic disadvantage along with other potential confounders such as test reliability, ancillary tests, and glaucoma surgeries on racial disparity in the frequency of VFT. Methods: The records of all subjects with a diagnosis of glaucoma who received VFT at an academic, tertiary care facility from January 2018 to December 2021 were accessed. Analysis was performed to compare VFT frequency, the total number of office visits (DoS), and the ratio of VFT frequency to DoS (VFT/DoS) across self-reported races while controlling for sex, age, socioeconomic disadvantage (Area Deprivation Index), VF reliability indicators and baseline mean deviation, optical coherence tomography frequency, and glaucoma surgeries. Results: Among the 2654 subjects (1515 White, 782 Black, and 357 Asian) included in this study, Black subjects had the worst socioeconomic status and disease severity at baseline. They also experienced a 3% lower VFT/DoS ratio compared to White subjects (P = 0.031). Asian subjects had a 5% lower VFT/DoS ratio compared to White subjects (P = 0.015). Discussion: We identified racial disparity in performing VFT in subjects with glaucoma even when multiple confounders were considered. Further investigation is necessary to identify other race-associated factors to work toward reducing racial disparities in VFT. Translational Relevance: Black and Asian subjects with glaucoma receive fewer VFT per visit compared to White subjects even when considering socioeconomic disadvantage and disease severity.

Peripapillary Atrophy Area as an Indicator of Glaucomatous Structural and Functional Progression.

Purpose: To determine whether peripapillary atrophy (PPA) area is an indicator of glaucomatous structural and functional damage and progression. Methods: In this retrospective longitudinal analysis from ongoing prospective study we qualified 71 eyes (50 subjects) with glaucoma. All subjects had a comprehensive ophthalmic examination, visual field (VF), and spectral-domain optical coherence tomography (OCT) testing in at least three visits. PPA was manually delineated on en face OCT optic nerve head scans, while observing the corresponding cross-sectional images, as the hyper-reflective area contiguous with the optic disc. Results: The mean follow-up duration was 4.4 ± 1.4 years with an average of 6.8 ± 2.2 visits. At baseline, PPA area was significantly associated only with VF's mean deviation (MD; P = 0.041), visual field index (VFI; P = 0.041), superior ganglion cell inner plexiform layer (GCIPL; P = 0.011), and disc area (P = 0.011). Longitudinally, PPA area was negatively and significantly associated with MD (P = 0.015), VFI (P = 0.035), GCIPL (P = 0.009), superior GCIPL (P = 0.034), and disc area (P = 0.007, positive association). Conclusions: Longitudinal change in PPA area is an indicator of glaucomatous structural and functional progression but PPA area at baseline cannot predict future progression. Translational Relevance: Longitudinal changes in peripapillary atrophy area measured by OCT can be an indicator of structural and functional glaucoma progression.

Early inner plexiform layer thinning and retinal nerve fiber layer thickening in excitotoxic retinal injury using deep learning-assisted optical coherence tomography.

Excitotoxicity from the impairment of glutamate uptake constitutes an important mechanism in neurodegenerative diseases such as Alzheimer's, multiple sclerosis, and Parkinson's disease. Within the eye, excitotoxicity is thought to play a critical role in retinal ganglion cell death in glaucoma, diabetic retinopathy, retinal ischemia, and optic nerve injury, yet how excitotoxic injury impacts different retinal layers is not well understood. Here, we investigated the longitudinal effects of N-methyl-D-aspartate (NMDA)-induced excitotoxic retinal injury in a rat model using deep learning-assisted retinal layer thickness estimation. Before and after unilateral intravitreal NMDA injection in nine adult Long Evans rats, spectral-domain optical coherence tomography (OCT) was used to acquire volumetric retinal images in both eyes over 4 weeks. Ten retinal layers were automatically segmented from the OCT data using our deep learning-based algorithm. Retinal degeneration was evaluated using layer-specific retinal thickness changes at each time point (before, and at 3, 7, and 28 days after NMDA injection). Within the inner retina, our OCT results showed that retinal thinning occurred first in the inner plexiform layer at 3 days after NMDA injection, followed by the inner nuclear layer at 7 days post-injury. In contrast, the retinal nerve fiber layer exhibited an initial thickening 3 days after NMDA injection, followed by normalization and thinning up to 4 weeks post-injury. Our results demonstrated the pathological cascades of NMDA-induced neurotoxicity across different layers of the retina. The early inner plexiform layer thinning suggests early dendritic shrinkage, whereas the initial retinal nerve fiber layer thickening before subsequent normalization and thinning indicates early inflammation before axonal loss and cell death. These findings implicate the inner plexiform layer as an early imaging biomarker of excitotoxic retinal degeneration, whereas caution is warranted when interpreting the ganglion cell complex combining retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses in conventional OCT measures. Deep learning-assisted retinal layer segmentation and longitudinal OCT monitoring can help evaluate the different phases of retinal layer damage upon excitotoxicity.

LIMBARE: An Advanced Linear Mixed-Effects Breakpoint Analysis With Robust Estimation Method With Applications to Longitudinal Ophthalmic Studies.

Purpose: Broken stick analysis is a widely used approach for detecting unknown breakpoints where the association between measurements is nonlinear. We propose LIMBARE, an advanced linear mixed-effects breakpoint analysis with robust estimation, especially designed for longitudinal ophthalmic studies. LIMBARE accommodates repeated measurements from both eyes and over time, and it effectively addresses the presence of outliers. Methods: The model setup of LIMBARE and the computing algorithm for point and confidence interval estimates of the breakpoint were introduced. The performance of LIMBARE and other competing methods was assessed via comprehensive simulation studies and application to a longitudinal ophthalmic study with 216 eyes (145 subjects) followed for an average of 3.7 ± 1.3 years to examine the longitudinal association between structural and functional measurements. Results: In simulation studies, LIMBARE showed the smallest bias and mean squared error for estimating the breakpoint, with an empirical coverage probability of corresponding confidence interval estimates closest to the nominal level for scenarios with and without outlier data points. In the application to the longitudinal ophthalmic study, LIMBARE detected two breakpoints between visual field mean deviation (MD) and retinal nerve fiber layer thickness and one breakpoint between MD and cup-to-disc ratio, whereas the cross-sectional analysis approach detected only one and none, respectively. Conclusions: LIMBARE enhances breakpoint estimation accuracy in longitudinal ophthalmic studies, and the cross-sectional analysis approach is not recommended for future studies. Translational Relevance: Our proposed method and companion R package provide a valuable computational tool for advancing longitudinal ophthalmology research and exploring the association relationships among ophthalmic variables.

Motion Contrast, Phase Gradient, and Simultaneous OCT Images Assist in the Interpretation of Dark-Field Images in Eyes with Retinal Pathology.

The cellular-level visualization of retinal microstructures such as blood vessel wall components, not available with other imaging modalities, is provided with unprecedented details by dark-field imaging configurations; however, the interpretation of such images alone is sometimes difficult since multiple structural disturbances may be present in the same time. Particularly in eyes with retinal pathology, microstructures may appear in high-resolution retinal images with a wide range of sizes, sharpnesses, and brightnesses. In this paper we show that motion contrast and phase gradient imaging modalities, as well as the simultaneous acquisition of depth-resolved optical coherence tomography (OCT) images, provide additional insight to help understand the retinal neural and vascular structures seen in dark-field images and may enable improved diagnostic and treatment plans.

Assessment of Remote Training, At-Home Testing, and Test-Retest Variability of a Novel Test for Clustered Virtual Reality Perimetry.

OBJECTIVE: To assess the feasibility of remotely training glaucoma patients to take a 10-session clustered virtual reality (VR) visual field (VF) test (Vivid Vision Perimetry [VVP-10]) at home, analyze results for test-retest variability, and assess correspondence with conventional perimetry. DESIGN: Cross-sectional study. SUBJECTS: Twenty-one subjects with glaucoma were enrolled and included in the feasibility assessment of remote training. Thirty-six eyes were used for test-retest analysis and determination of concordance with the Humphrey Field Analyzer (HFA). METHODS: Subjects were provided with a mobile VR headset containing the VVP-10 test software and trained remotely via video conferencing. Subjects were instructed to complete 10 sessions over a 14-day period. MAIN OUTCOME MEASURES: Feasibility was determined by the number of subjects who were able to independently complete VVP-10 over the 14-day period after 1 remote training session. The intraclass correlation coefficient (ICC) for average fraction seen across 10 sessions and the standard error (SE) of the mean were primary outcome measures for assessing test-retest variability. Correlation with HFA mean sensitivity (MS) across eyes, was a secondary outcome measure. RESULTS: Twenty subjects (95%) successfully completed the VVP-10 test series after 1 training session. The ICC for VVP-10 was 0.95 (95% confidence interval [CI], 0.92-0.97). The mean SE in units of fraction seen was 0.012. The Spearman correlations between VVP-10 average fraction seen and HFA MS were 0.87 (95% CI, 0.66-0.98) for moderate-to-advanced glaucoma eyes, and decreased to 0.67 (95% CI, 0.28-0.94) when all eyes were included. CONCLUSIONS: Remote training of patients at home is feasible, and subsequent remote clustered VF testing using VVP-10 by patients on their own, without any further interactions with caregivers or study staff, was possible. At-home VVP-10 results demonstrated low test-retest variability. Future studies must be conducted to determine if VVP-10, taken at home as convenient for the patient, may be a viable supplement to provide equivalent or complementary results to that of standard in-clinic assessment of visual function in glaucoma. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Interim Analysis of Clinical Outcomes with Open versus Closed Conjunctival Implantation of the XEN45 Gel Stent.

OBJECTIVE: To examine the longitudinal postoperative outcomes of open versus closed conjunctiva implantation of the XEN45 gel stent. DESIGN: Retrospective multicenter study. SUBJECTS: One hundred ninety-three patients with glaucoma underwent XEN45 implantation via an open or closed conjunctiva approach. METHODS: Data on patient demographics; diagnoses; preoperative and postoperative clinical data; outcome measures, including intraocular pressure (IOP); use of glaucoma medications; visual acuity; and complications were collected. Statistical analyses were performed with P < 0.05 as significant. MAIN OUTCOME MEASURES: Failure was defined as < 20% reduction in IOP from the medicated baseline or a IOP of > 21 mmHg at 2 consecutive visits at postoperative month 1 and beyond, the need for subsequent operative intervention or additional glaucoma surgery, or a catastrophic event, such as loss of light perception. Eyes that had not failed by these criteria and were not on glaucoma medications were considered complete successes. Overall success was defined as those who achieved success either with or without topical medications. RESULTS: Patients were followed for an average of 17 months. Complete success was achieved in 42.5% and 24.7% of the open and closed groups, respectively (P = 0.01). Overall success was achieved in 64.2% and 37.0% of the open and closed groups, respectively (P < 0.001) at the last follow-up. Bleb needling was performed in 12.4% of eyes in the open group compared with 40% of eyes in the closed group. An IOP spike of ≥ 10 mmHg was twice as likely to occur in the closed group compared with the open group during the postoperative period (40% vs. 18%; P = 0.001). CONCLUSIONS: Implantation of XEN45 with opening of the conjunctiva resulted in a lower IOP with greater success and lower needling rate compared with those achieved with the closed conjunctiva technique. Similar rates of postoperative complications and vision loss were noted in each group. Although both procedures provide substantial IOP reduction, the open technique appears to result in higher success rates and fewer postoperative interventions. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality.

Purpose: Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods. Method: We examined the ophthalmic healthcare disparities at a population level using electronic medical records data from a study cohort (N = 785) receiving care at an academic institute. Regression-based TL models were usesd, transferring valuable information from the dominant racial group (White) to improve visual field mean deviation (MD) rate of change prediction particularly for data-disadvantaged African American (AA) and Asian racial groups. Prediction results of TL models were compared with two conventional approaches. Results: Disparities in socioeconomic status and baseline disease severity were observed among the AA and Asian racial groups. The TL approach achieved marked to comparable improvement in prediction accuracy compared to the two conventional approaches as evident by smaller mean absolute errors or mean square errors. TL identified distinct key features of visual field MD rate of change for each racial group. Conclusions: The study introduces a novel application of TL that improved reliability of the analysis in comparison with conventional methods, especially in small sample size groups. This can improve assessment of healthcare disparity and subsequent remedy approach. Translational Relevance: TL offers an equitable and efficient approach to mitigate healthcare disparities analysis by enhancing prediction performance for data-disadvantaged group.

Can Glaucoma Suspect Data Help to Improve the Performance of Glaucoma Diagnosis?

Purpose: The presence of imbalanced datasets in medical applications can negatively affect deep learning methods. This study aims to investigate how the performance of convolutional neural networks (CNNs) for glaucoma diagnosis can be improved by addressing imbalanced learning issues through utilizing glaucoma suspect samples, which are often excluded from studies because they are a mixture of healthy and preperimetric glaucomatous eyes, in a semi-supervised learning approach. Methods: A baseline 3D CNN was developed and trained on a real-world glaucoma dataset, which is naturally imbalanced (like many other real-world medical datasets). Then, three methods, including reweighting samples, data resampling to form balanced batches, and semi-supervised learning on glaucoma suspect data were applied to practically assess their impacts on the performances of the trained methods. Results: The proposed method achieved a mean accuracy of 95.24%, an F1 score of 97.42%, and an area under the curve of receiver operating characteristic (AUC ROC) of 95.64%, whereas the corresponding results for the traditional supervised training using weighted cross-entropy loss were 92.88%, 96.12%, and 92.72%, respectively. The obtained results show statistically significant improvements in all metrics. Conclusions: Exploiting glaucoma suspect eyes in a semi-supervised learning method coupled with resampling can improve glaucoma diagnosis performance by mitigating imbalanced learning issues. Translational Relevance: Clinical imbalanced datasets may negatively affect medical applications of deep learning. Utilizing data with uncertain diagnosis, such as glaucoma suspects, through a combination of semi-supervised learning and class-imbalanced learning strategies can partially address the problems of having limited data and learning on imbalanced datasets.

Segmentation-Free OCT-Volume-Based Deep Learning Model Improves Pointwise Visual Field Sensitivity Estimation.

Purpose: The structural changes measured by optical coherence tomography (OCT) are related to functional changes in visual fields (VFs). This study aims to accurately assess the structure-function relationship and overcome the challenges brought by the minimal measurable level (floor effect) of segmentation-dependent OCT measurements commonly used in prior studies. Methods: We developed a deep learning model to estimate the functional performance directly from three-dimensional (3D) OCT volumes and compared it to the model trained with segmentation-dependent two-dimensional (2D) OCT thickness maps. Moreover, we proposed a gradient loss to utilize the spatial information of VFs. Results: Our 3D model was significantly better than the 2D model both globally and pointwise regarding both mean absolute error (MAE = 3.11 + 3.54 vs. 3.47 ± 3.75 dB, P < 0.001) and Pearson's correlation coefficient (0.80 vs. 0.75, P < 0.001). On a subset of test data with floor effects, the 3D model showed less influence from floor effects than the 2D model (MAE = 5.24 ± 3.99 vs. 6.34 ± 4.58 dB, P < 0.001, and correlation 0.83 vs. 0.74, P < 0.001). The gradient loss improved the estimation error for low-sensitivity values. Furthermore, our 3D model outperformed all prior studies. Conclusions: By providing a better quantitative model to encapsulate the structure-function relationship more accurately, our method may help deriving VF test surrogates. Translational Relevance: DL-based VF surrogates not only benefit patients by reducing the testing time of VFs but also allow clinicians to make clinical judgments without the inherent limitations of VFs.

GABA decrease is associated with degraded neural specificity in the visual cortex of glaucoma patients.

Glaucoma is an age-related neurodegenerative disease of the visual system, affecting both the eye and the brain. Yet its underlying metabolic mechanisms and neurobehavioral relevance remain largely unclear. Here, using proton magnetic resonance spectroscopy and functional magnetic resonance imaging, we investigated the GABAergic and glutamatergic systems in the visual cortex of glaucoma patients, as well as neural specificity, which is shaped by GABA and glutamate signals and underlies efficient sensory and cognitive functions. Our study shows that among the older adults, both GABA and glutamate levels decrease with increasing glaucoma severity regardless of age. Further, our study shows that the reduction of GABA but not glutamate predicts the neural specificity. This association is independent of the impairments on the retina structure, age, and the gray matter volume of the visual cortex. Our results suggest that glaucoma-specific decline of GABA undermines neural specificity in the visual cortex and that targeting GABA could improve the neural specificity in glaucoma.

Follow-up Rates After Teleretinal Screening for Diabetic Retinopathy: Assessing Patient Barriers to Care.

Purpose: To study patient follow-up after they engage in a teleretinal screening program and to understand potential barriers to care. Methods: This was a retrospective analysis and a prospective study of telephone-based patient interviews of outpatients screened for diabetic retinopathy (DR) through a teleretinal referral system. Results: Of 2761 patients screened through a teleretinal referral program, 123 (4.5%) had moderate nonproliferative DR (NPDR), 83 (3.0%) had severe NPDR, and 31 (1.1%) had proliferative DR. Of the 114 patients with severe NPDR or worse, 67 (58.8%) saw an ophthalmologist within 3 months of referral. Eighty percent of interviewed patients reported they were not aware of the need for follow-up eye appointments. Conclusions: Of patients with severe retinopathy or worse, 58.8% presented for in-person evaluation and treatment within 3 months of screening. Although this result was negatively affected by factors related to the COVID-19 pandemic, key elements of patient education and improved referral strategies to facilitate in-person treatment are essential to improving follow-up after patients engage in telescreening.

Under Pressure: Lamina Cribrosa Pore Path Tortuosity in Response to Acute Pressure Modulation.

Purpose: Lamina cribrosa (LC) deformation is hypothesized to play a major role in glaucoma pathogenesis. The purpose of this study was to determine in vivo how varying intraocular pressure (IOP) under fixed intracranial pressure (ICP), and vice versa, deforms the pore paths throughout the LC volume. Methods: Spectral-domain optical coherence tomography scans of the optic nerve head were acquired from healthy adult rhesus monkeys under different pressures. IOP and ICP were controlled with gravity-based perfusion systems into the anterior chamber and lateral ventricle, respectively. IOP and ICP were modulated from baseline to high (19-30 mmHg) and highest (35-50 mmHg) levels while maintaining a fixed ICP of 8 to 12 mmHg and IOP of 15 mmHg, respectively. After three-dimensional registration and segmentation, the paths of pores visible in all settings were tracked based on their geometric centroids. Pore path tortuosity was defined as the measured distance divided by the minimal distance between the most anterior and posterior centroids. Results: The median pore tortuosity at baseline varied among the eyes (range, 1.16-1.68). For the IOP effect under fixed ICP (six eyes, five animals), two eyes showed statistically significant increased tortuosity and one showed a decrease (P < 0.05, mixed-effects model). No significant change was detected in three eyes. When modulating ICP under fixed IOP (five eyes, four animals), a similar response pattern was detected. Conclusions: Baseline pore tortuosity and the response to acute pressure increase vary substantially across eyes. Translational Relevance: LC pore path tortuosity could be associated with glaucoma susceptibility.

Adaptive spectroscopic visible-light optical coherence tomography for clinical retinal oximetry.

BACKGROUND: Retinal oxygen saturation (sO2) provides essential information about the eye's response to pathological changes that can result in vision loss. Visible-light optical coherence tomography (vis-OCT) is a noninvasive tool that has the potential to measure retinal sO2 in a clinical setting. However, its reliability is currently limited by unwanted signals referred to as spectral contaminants (SCs), and a comprehensive strategy to isolate true oxygen-dependent signals from SCs in vis-OCT is lacking. METHODS: We develop an adaptive spectroscopic vis-OCT (ADS-vis-OCT) technique that can adaptively remove SCs and accurately measure sO2 under the unique conditions of each vessel. We also validate the accuracy of ADS-vis-OCT using ex vivo blood phantoms and assess its repeatability in the retina of healthy volunteers. RESULTS: In ex vivo blood phantoms, ADS-vis-OCT agrees with a blood gas machine with only a 1% bias in samples with sO2 ranging from 0% to 100%. In the human retina, the root mean squared error between sO2 values in major arteries measured by ADS-vis-OCT and a pulse oximeter is 2.1% across 18 research participants. Additionally, the standard deviations of repeated ADS-vis-OCT measurements of sO2 values in smaller arteries and veins are 2.5% and 2.3%, respectively. Non-adaptive methods do not achieve comparable repeatabilities from healthy volunteers. CONCLUSIONS: ADS-vis-OCT effectively removes SCs from human images, yielding accurate and repeatable sO2 measurements in retinal arteries and veins with varying diameters. This work could have important implications for the clinical use of vis-OCT to manage eye diseases.

Numerous diseases that cause blindness are associated with disrupted oxygen consumption in the retina, the part of the eye that senses light. This highlights the importance of accurately measuring oxygen consumption in the clinic. To address this challenge, we developed a method to analyze images of the retina which have been collected using visible-light optical coherence tomography, a non-invasive imaging method. Our approach achieves accurate oxygen level measurements in blood samples and in healthy volunteers. With further testing, our approach may prove useful in the clinical management of several diseases that cause blindness, allowing clinicians to more accurately diagnose disease and monitor the health of the eye.