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Photoreceptors (PRs) and retinal pigment epithelial (RPE) cells form a functional unit called the PR-RPE complex. The PR-RPE complex plays a critical role in maintaining retinal homeostasis and function, and the quantification of its structure and topographical arrangement across the macula are important for understanding the etiology, mechanisms, and progression of many retinal diseases. However, the three-dimensional cellular morphology of the PR-RPE complex in living human eyes has not been completely described due to limitations in imaging techniques. We used the cellular resolution and depth-sectioning capabilities of a custom, high-speed Fourier domain mode-locked laser-based adaptive optics–optical coherence tomography (FDML-AO-OCT) platform to characterize human PR-RPE complex topography across the temporal macula from eleven healthy volunteers. With the aid of a deep learning algorithm, key metrics were extracted from the PR-RPE complex of averaged AO-OCT volumes including PR and RPE cell density, PR outer segment length (OSL), and PR/RPE ratio. We found a tight grouping among our cohort for PR density, with a mean (±SD) value of 53,329 (±8106) cells/mm2 at 1° decreasing to 8669 (±737) cells/mm2 at 12°. We observed a power function relationship between eccentricity and both PR density and PR/RPE ratio. We found similar variability in our RPE density measures, with a mean value of 7335 (±681) cells/mm2 at 1° decreasing to 5547 (±356) cells/mm2 at 12°, exhibiting a linear relationship with a negative slope of −123 cells/mm2 per degree. OSL monotonically decreased from 33.3 (±2.4) µm at 1° to 18.0 (±1.8) µm at 12°, following a second-order polynomial relationship. PR/RPE ratio decreased from 7.3 (±0.9) µm at 1° to 1.5 (±0.1) µm at 12°. The normative data from this investigation will help lay a foundation for future studies of retinal pathology.

Purpose: To characterize changes in subfoveal choroidal thickness in preterm infants from 30 to 60 weeks' postmenstrual age (PMA). Design: The prospective, observational Study of Eye Imaging in Preterm infantS (BabySTEPS) enrolled infants eligible for retinopathy of prematurity screening per the American Association of Pediatrics guidelines. Subjects: Infants imaged with an investigational, handheld OCT at ≥ 4 distinct imaging sessions between 30 to 60 weeks' PMA as part of BabySTEPS. Methods: Average choroidal thickness across the central subfoveal 1 mm in each eye at each time point was measured using custom segmentation software, and errors were manually corrected by a trained grader. We prospectively collected birth history data. A segmented mixed model was used to analyze the change in choroidal thickness as a function of PMA, birth weight, and gestational age (GA). Main Outcome Measures: Characterization of normative subfoveal choroidal thickness values and choroidal growth rate between 30 to 60 weeks' PMA. Results: We included 592 imaging sessions of 79 preterm infants (152 eyes). Mean (± standard deviation) GA was 27.5 ± 2.5 weeks. Mean choroidal thickness was 141.4 ± 34.5 μm at 30 weeks, 272.2 ± 83.9 μm at 38 weeks, and 306.2 ± 77.4 μm between 56 and 60 weeks. Between 30 and 60 weeks' PMA, choroidal growth followed a biphasic model, with a linear growth rate of 14.8 μm per week (95% confidence interval [CI], 13.6–16.0) from 30 until 38.4 weeks, then cessation of growth, with a growth rate of 0.3 μm per week (95% CI, −1.1 to 1.6) from 38.4 to 60 weeks. Infants with extremely low birth weight (ELBW; < 1000 g) and extremely preterm (GA < 28 weeks) infants had significantly slower initial growth rates compared with very low and low birth weight and very preterm and preterm infants (ELBW 13.0 vs. 21.0 μm per week; P < 0.0001 and extremely preterm 13.2 vs. 18.0 μm per week; P = 0.003). Conclusions: Preterm infant choroidal thickness experiences rapid linear growth from 30 to 38 weeks' PMA, at which time growth nearly stops. These foundational measurements and identification of the impact of extremes of low birth weight and prematurity on choroidal development will be essential as researchers begin to understand the role of choroidal development in ocular and retinal health in human infants. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Purpose: To develop a fully-automatic hybrid algorithm to jointly segment and quantify biomarkers of polypoidal choroidal vasculopathy (PCV) on indocyanine green angiography (ICGA) and spectral domain-OCT (SD-OCT) images. Design: Evaluation of diagnostic test or technology. Participants: Seventy-two participants with PCV enrolled in clinical studies at Singapore National Eye Center. Methods: The dataset consisted of 2-dimensional (2-D) ICGA and 3-dimensional (3-D) SD-OCT images which were spatially registered and manually segmented by clinicians. A deep learning-based hybrid algorithm called PCV-Net was developed for automatic joint segmentation of biomarkers. The PCV-Net consisted of a 2-D segmentation branch for ICGA and 3-D segmentation branch for SD-OCT. We developed fusion attention modules to connect the 2-D and 3-D branches for effective use of the spatial correspondence between the imaging modalities by sharing learned features. We also used self-supervised pretraining and ensembling to further enhance the performance of the algorithm without the need for additional datasets. We compared the proposed PCV-Net to several alternative model variants. Main Outcome Measures: The PCV-Net was evaluated based on the Dice similarity coefficient (DSC) of the segmentations and the Pearson’s correlation and absolute difference of the clinical measurements obtained from the segmentations. Manual grading was used as the gold standard. Results: The PCV-Net showed good performance compared to manual grading and alternative model variants based on both quantitative and qualitative analyses. Compared to the baseline variant, PCV-Net improved the DSC by 0.04 to 0.43 across the different biomarkers, increased the correlations, and decreased the absolute differences of clinical measurements of interest. Specifically, the largest average (mean ± standard error) DSC improvement was for intraretinal fluid, from 0.02 ± 0.00 (baseline variant) to 0.45 ± 0.06 (PCV-Net). In general, improving trends were observed across the model variants as more technical specifications were added, demonstrating the importance of each aspect of the proposed method. Conclusion: The PCV-Net has the potential to aid clinicians in disease assessment and research to improve clinical understanding and management of PCV. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Purpose: To describe spectral-domain OCT (SD-OCT) features, age, gender, and systemic variables that may be used in machine/deep learning studies to identify high-risk patient subpopulations with high risk of progression to geographic atrophy (GA) and visual acuity (VA) loss in the short term. Design: Prospective, longitudinal study. Participants: We analyzed imaging data from patients with intermediate age-related macular degeneration (iAMD) (N = 316) enrolled in the Age-Related Eye Disease Study 2 (AREDS2) Ancillary SD-OCT with adequate SD-OCT imaging for repeated measures. Methods: Qualitative and quantitative multimodal variables from the database were derived at each yearly visit over 5 years. Based on statistical analyses developed in the field of cardiology, an algorithm was developed and used to select person-years without GA on color fundus photography or SD-OCT at baseline. The analysis used machine learning approaches to generate classification trees. Eyes were stratified as low, average, above average, and high risk in 1 or 2 years, based on OCT and demographic features by the risk of GA development or decreased VA by 5+ and 10+ letters. Main Outcome Measures: New onset of SD-OCT–determined GA and VA loss. Results: We identified multiple retinal and subretinal SD-OCT and demographic features from the baseline visit, each of which independently conveyed low to high risk of new-onset GA or VA loss on each of the follow-up visits at 1 or 2 years. Conclusions: We propose a risk-stratified classification of iAMD based on the combination of OCT-derived retinal features, age, gender, and systemic variables for progression to OCT-determined GA or VA loss. After external validation, the composite early end points may be used as exclusion or inclusion criteria for future clinical studies of iAMD focused on prevention of GA progression or VA loss.

Glucocorticoids are widely used as an ophthalmic medication. A common, sight-threatening adverse event of glucocorticoid usage is ocular hypertension, caused by dysfunction of the conventional outflow pathway. We report that netarsudil, a rho-kinase inhibitor, decreased glucocorticoid-induced ocular hypertension in patients whose intraocular pressures were poorly controlled by standard medications. Mechanistic studies in our established mouse model of glucocorticoid-induced ocular hypertension show that netarsudil both prevented and reduced intraocular pressure elevation. Further, netarsudil attenuated characteristic steroid-induced pathologies as assessed by quantification of outflow function and tissue stiffness, and morphological and immunohistochemical indicators of tissue fibrosis. Thus, rho-kinase inhibitors act directly on conventional outflow cells to prevent or attenuate fibrotic disease processes in glucocorticoid-induced ocular hypertension in an immune-privileged environment. Moreover, these data motivate the need for a randomized prospective clinical study to determine whether netarsudil is indeed superior to first-line anti-glaucoma drugs in lowering steroid-induced ocular hypertension.

Abstract Ancient Egyptian mummies were often covered with an outer casing, panels and masks made from cartonnage: a lightweight material made from linen, plaster, and recycled papyrus held together with adhesive. Egyptologists, papyrologists, and historians aim to recover and read extant text on the papyrus contained within cartonnage layers, but some methods, such as dissolving mummy casings, are destructive. The use of an advanced range of different imaging modalities was investigated to test the feasibility of non-destructive approaches applied to multi-layered papyrus found in ancient Egyptian mummy cartonnage. Eight different techniques were compared by imaging four synthetic phantoms designed to provide robust, well-understood, yet relevant sample standards using modern papyrus and replica inks. The techniques include optical (multispectral imaging with reflection and transillumination, and optical coherence tomography), X-ray (X-ray fluorescence imaging, X-ray fluorescence spectroscopy, X-ray micro computed tomography and phase contrast X-ray) and terahertz-based approaches. Optical imaging techniques were able to detect inks on all four phantoms, but were unable to significantly penetrate papyrus. X-ray-based techniques were sensitive to iron-based inks with excellent penetration but were not able to detect carbon-based inks. However, using terahertz imaging, it was possible to detect carbon-based inks with good penetration but with less sensitivity to iron-based inks. The phantoms allowed reliable and repeatable tests to be made at multiple sites on three continents. The tests demonstrated that each imaging modality needs to be optimised for this particular application: it is, in general, not sufficient to repurpose an existing device without modification. Furthermore, it is likely that no single imaging technique will to be able to robustly detect and enable the reading of text within ancient Egyptian mummy cartonnage. However, by carefully selecting, optimising and combining techniques, text contained within these fragile and rare artefacts may eventually be open to non-destructive imaging, identification, and interpretation.

Abstract Imaging with an adaptive optics scanning light ophthalmoscope (AOSLO) enables direct visualization of the cone photoreceptor mosaic in the living human retina. Quantitative analysis of AOSLO images typically requires manual grading, which is time consuming, and subjective; thus, automated algorithms are highly desirable. Previously developed automated methods are often reliant on ad hoc rules that may not be transferable between different imaging modalities or retinal locations. In this work, we present a convolutional neural network (CNN) based method for cone detection that learns features of interest directly from training data. This cone-identifying algorithm was trained and validated on separate data sets of confocal and split detector AOSLO images with results showing performance that closely mimics the gold standard manual process. Further, without any need for algorithmic modifications for a specific AOSLO imaging system, our fully-automated multi-modality CNN-based cone detection method resulted in comparable results to previous automatic cone segmentation methods which utilized ad hoc rules for different applications. We have made free open-source software for the proposed method and the corresponding training and testing datasets available online.

Introduction: Anterior lens capsule vascularity (ALCV) is resorbed in the developing fetus from 27 to 35 weeks gestation. In this pilot study, we evaluated the feasibility and validity of combining smartphone ophthalmoscope videos of ALCV and image analysis for gestational age estimation.Methods: ALCV videos were captured longitudinally in preterm neonates from delivery using a PanOptic® Ophthalmoscope with an iExaminer® adapter (Welch-Allyn). ALCV video frames were manually selected and quantified using semi-automatic image analysis. A predictive model based on ALCV features was compared to gold-standard ultrasound gestational age estimates.Results: A total of 64 image-capture sessions were carried out in 24 neonates. Ultrasound-estimated gestational age and ALCV-predicted gestational age estimates indicate that the two methods are similar (r = 0.78, p < 0.0001). ALCV estimates of gestational age were within 0.11 ± 1.3 weeks of ultrasound estimates. In the final model, gestational age was predicted within ± 1 week for 54% and within ± 2 weeks for 86% of the measures.Conclusions: This novel application of smartphone ophthalmoscopy and ALCV image analysis may provide a safe, accurate and non-invasive technology to estimate postnatal gestational age, especially in low income countries where gestational age may not be known at birth.

Although drusen have long been acknowledged as a primary hallmark of dry age-related macular degeneration (AMD) their role in the disease remains unclear. We hypothesize that drusen accumulation increases the barrier to metabolite transport ultimately resulting in photoreceptor cell death. To investigate this hypothesis, a computational model was developed to evaluate steady-state oxygen distribution in the retina. Optical coherence tomography images from fifteen AMD patients and six control subjects were segmented and translated into 3D in silico representations of retinal morphology. A finite element model was then used to determine the steady-state oxygen distribution throughout the retina for both generic and patient-specific retinal morphology. Oxygen levels were compared to the change in retinal thickness at a later time point to observe possible correlations. The generic finite element model of oxygen concentration in the retina agreed closely with both experimental measurements from literature and clinical observations, including the minimal pathological drusen size identified by AREDS (64 μm). Modeling oxygen distribution in the outer retina of AMD patients showed a substantially stronger correlation between hypoxia and future retinal thinning (Pearson correlation coefficient, r = 0.2162) than between drusen height and retinal thinning (r = 0.0303) indicating the potential value of this physiology-based approach. This study presents proof-of-concept for the potential utility of finite element modeling in evaluating retinal health and also suggests a potential link between transport and AMD pathogenesis. This strategy may prove useful as a prognostic tool for predicting the clinical risk of AMD progression.

ABSTRACT IMPACT: Despite its importance in systemic diseases such as diabetes, the eye is notably difficult to examine for non-specialists; this study introduces a fully automated approach for eye disease screening, coupling a deep learning algorithm with a robotically-aligned optical coherence tomography system to improve eye care in non-ophthalmology settings. OBJECTIVES/GOALS: This study aims to develop and test a deep learning (DL) method to classify images acquired from a robotically-aligned optical coherence tomography (OCT) system as normal vs. abnormal. The long-term goal of our study is to integrate artificial intelligence and robotic eye imaging to fully automate eye disease screening in diverse clinical settings. METHODS/STUDY POPULATION: Between August and October 2020, patients seen at the Duke Eye Center and healthy volunteers (age ≥18) were imaged with a custom, robotically-aligned OCT (RAOCT) system following routine eye exam. Using transfer learning, we adapted a preexisting convolutional neural network to train a DL algorithm to classify OCT images as normal vs. abnormal. The model was trained and validated on two publicly available OCT datasets and two of our own RAOCT volumes. For external testing, the top-performing model based on validation was applied to a representative averaged B-scan from each of the remaining RAOCT volumes. The model’s performance was evaluated against a reference standard of clinical diagnoses by retina specialists. Saliency maps were created to visualize the areas contributing most to the model predictions. RESULTS/ANTICIPATED RESULTS: The training and validation datasets included 87,697 OCT images, of which 59,743 were abnormal. The top-performing DL model had a training accuracy of 96% and a validation accuracy of 99%. For external testing, 43 eyes of 27 subjects were imaged with the robotically-aligned OCT system. Compared to clinical diagnoses, the model correctly labeled 18 out of 22 normal averaged B-scans and 18 out of 21 abnormal averaged B-scans. Overall, in the testing set, the model had an AUC for the detection of pathology of 0.92, an accuracy of 84%, a sensitivity of 86%, and a specificity of 82%. For the correctly predicted scans, saliency maps identified the areas contributing most to the DL algorithm’s predictions, which matched the regions of greatest clinical importance. DISCUSSION/SIGNIFICANCE OF FINDINGS: This is the first study to develop and apply a DL model to images acquired from a self-aligning OCT system, demonstrating the potential of integrating DL and robotic eye imaging to automate eye disease screening. We are working to translate this technology for use in emergency departments and primary care, where it will have the greatest impact.

Abstract Introduction Retinal structural changes in subjects with mild cognitive impairment (MCI) remain a subject of controversy. Methods We investigated the correlation between optical coherence tomography (OCT) of the retinal sublayers, including the retinal nerve fiber layer (RNFL), and cognitive function in subjects with amnestic MCI and compared the OCT findings with matched controls. We also performed a meta‐analysis of the world literature using a random‐effects model. Results We found no statistically significant differences in OCT between amnestic MCI (aMCI) and controls. In aMCI subjects, we found an inverse relationship between RNFL thickness and two cognitive tests (delayed story recall and a word‐list learning test and the word‐list test). The meta‐analysis revealed a statistically significant decrease in RNFL thickness in MCI subjects. Discussion The inverse relationship between cognitive testing and RNFL thickness suggests that retinal involvement may include paradoxically increased thickness of the RNFL, which could suggest gliotic reactive changes.

Inner retina in Alzheimer's Disease (AD) may experience neuroinflammation resulting in atrophy. The objective of our study was to determine whether retinal GCIPL (ganglion cell-inner plexiform layer) or nerve fiber layer (NFL) thickness may serve as noninvasive biomarkers to diagnose AD. This cross-sectional case-control study enrolled 15 mild cognitive impairment (MCI) patients, 15 mild-moderate AD patients, and 18 cognitively normal adults. NFL and GCIPL thicknesses on optical coherence tomography (OCT) were measured using Duke Optical Coherence Tomography Retinal Analysis Program (DOCTRAP) and Spectralis software. We demonstrated that regional thicknesses of NFL or GCIPL on macular or nerve OCTs did not differ between groups. However, a multi-variate regression analysis identified macular areas with a significant thickening or thinning in NFL and GCIPL in MCI and AD patients. Our primary findings controvert previous reports of thinner NFL in moderate-to-severe AD. The areas of thickening of GCIPL and NFL in the macula adjacent to areas of thinning, as revealed by a more complex statistical model, suggest that NFL and GCIPL may undergo dynamic changes during AD progression.