A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration

Deep Neural Networks for Automated Outer Plexiform Layer Subsidence Detection on Retinal OCT of Patients With Intermediate AMD

Purpose

The subsidence of the outer plexiform layer (OPL) is an important imaging biomarker on optical coherence tomography (OCT) associated with early outer retinal atrophy and a risk factor for progression to geographic atrophy in patients with intermediate age-related macular degeneration (AMD). Deep neural networks (DNNs) for OCT can support automated detection and localization of this biomarker.

Methods

The method predicts potential OPL subsidence locations on retinal OCTs. A detection module (DM) infers bounding boxes around subsidences with a likelihood score, and a classification module (CM) assesses subsidence presence at the B-scan level. Overlapping boxes between B-scans are combined and scored by the product of the DM and CM predictions. The volume-wise score is the maximum prediction across all B-scans. One development and one independent external data set were used with 140 and 26 patients with AMD, respectively.

Results

The system detected more than 85% of OPL subsidences with less than one false-positive (FP)/scan. The average area under the curve was 0.94 ± 0.03 for volume-level detection. Similar or better performance was achieved on the independent external data set.

Conclusions

DNN systems can efficiently perform automated retinal layer subsidence detection in retinal OCT images. In particular, the proposed DNN system detects OPL subsidence with high sensitivity and a very limited number of FP detections.

Translational Relevance

DNNs enable objective identification of early signs associated with high risk of progression to the atrophic late stage of AMD, ideally suited for screening and assessing the efficacy of the interventions aiming to slow disease progression.

Home Optical Coherence Tomography Monitoring for Neovascular Age-Related Macular Degeneration: Transformative Technology or Cool Toy?

The pending introduction of home-based optical coherence tomography (OCT) in managing neovascular age-related macular degeneration (nAMD) has sparked interesting debates. Advocates assert that home-based OCT will revolutionize care of patients with nAMD, while skeptics question its real-world viability and point out its potential drawbacks. This article delves into the dichotomy, presenting the "pro" argument highlighting the transformative potential of home OCT and the "con" perspective, which scrutinizes the limitations and challenges to adapting the technology to the real-world setting. By exploring both sides of the discourse, we aim to address the promises and complexities surrounding the role of home OCT in the management of nAMD.

American Society of Retina Specialists Clinical Practice Guidelines on Multimodal Imaging for Retinal Disease

Purpose: Advancements in retinal imaging have augmented our understanding of the pathology and structure-function relationships of retinal disease. No single diagnostic test is sufficient; rather, diagnostic and management strategies increasingly involve the synthesis of multiple imaging modalities. Methods: This literature review and editorial offer practical clinical guidelines for how the retina specialist can use multimodal imaging to manage retinal conditions. Results: Various imaging modalities offer information on different aspects of retinal structure and function. For example, optical coherence tomography (OCT) and B-scan ultrasonography can provide insights into the microstructural anatomy; fluorescein angiography (FA), indocyanine green angiography (ICGA), and OCT angiography (OCTA) can reveal vascular integrity and perfusion status; and near-infrared reflectance and fundus autofluorescence (FAF) can characterize molecular components within tissues. Managing retinal vascular diseases often includes fundus photography, OCT, OCTA, and FA to evaluate for macular edema, retinal ischemia, and the secondary complications of neovascularization (NV). OCT and FAF play a key role in diagnosing and treating maculopathies. FA, OCTA, and ICGA can help identify macular NV, posterior uveitis, and choroidal venous insufficiency, which guides treatment strategies. Finally, OCT and B-scan ultrasonography can help with preoperative planning and prognostication in vitreoretinal surgical conditions. Conclusions: Today, the retina specialist has access to numerous retinal imaging modalities that can augment the clinical examination to help diagnose and manage retinal conditions. Understanding the capabilities and limitations of each modality is critical to maximizing its clinical utility.

Subretinal transient hyporeflectivity in neovascular age-related macular degeneration and its response to a loading phase of aflibercept: PRECISE report 4

PURPOSE

To describe the prevalence of subretinal transient hyporeflectivity (STHR) in exudative neovascular age-related macular degeneration (nAMD) and its response to a loading phase of aflibercept.

METHODS

Optical coherence tomography (OCT) scans of treatment-naïve nAMD patients captured at baseline and after a loading phase of aflibercept were graded for presence of STHR, defined as a small, well-defined, round, subretinal, hyporeflective area, delimited between the ellipsoid zone (EZ) and the retinal pigmented epithelium/Bruch membrane complex. OCT parameters recorded were macular neovascularisation (MNV) subtypes, location of retinal fluids (subretinal fluid, SRF and intraretinal fluid, IRF), central retinal and choroidal thickness. Response was defined as absence of IRF and SRF. Factors associated with completely resolved STHR versus persistent STHR post-loading phase were compared.

RESULTS

2039 eyes of 1901 patients were analysed. STHR was observed in 79 eyes of 78 patients, with an estimated prevalence of 3.87% (95% CI 3.08-4.81%). STHR were seen in 44 type 1 MNV (56%), 27 with type 2 (34%), and 8 with type 3 (10%). At baseline, a total of 303 STHR were present, ranging between 1-22 per eye. The total number of STHR reduced significantly after the loading phase to 173 (p = 0.002). Complete disappearance of STHR was seen in 44 eyes (56%) and persistent STHR in the rest (44%).

CONCLUSIONS

STHR may represent a marker of low-grade exudation in nAMD eyes with good response to a loading phase of aflibercept. However, its potential role as an independent nAMD activity biomarker is limited as most resolve after the loading phase.

Anomaly guided segmentation: Introducing semantic context for lesion segmentation in retinal OCT using weak context supervision from anomaly detection

Automated lesion detection in retinal optical coherence tomography (OCT) scans has shown promise for several clinical applications, including diagnosis, monitoring and guidance of treatment decisions. However, segmentation models still struggle to achieve the desired results for some complex lesions or datasets that commonly occur in real-world, e.g. due to variability of lesion phenotypes, image quality or disease appearance. While several techniques have been proposed to improve them, one line of research that has not yet been investigated is the incorporation of additional semantic context through the application of anomaly detection models. In this study we experimentally show that incorporating weak anomaly labels to standard segmentation models consistently improves lesion segmentation results. This can be done relatively easy by detecting anomalies with a separate model and then adding these output masks as an extra class for training the segmentation model. This provides additional semantic context without requiring extra manual labels. We empirically validated this strategy using two in-house and two publicly available retinal OCT datasets for multiple lesion targets, demonstrating the potential of this generic anomaly guided segmentation approach to be used as an extra tool for improving lesion detection models.

Considerations for the Identification and Management of Geographic Atrophy: Recommendations from an Expert Panel

Newly approved treatments for patients with geographic atrophy are changing the treatment paradigm, highlighting the need for eye care providers (ECPs) to have a set of recommendations on how to best manage GA patients. Here, we outline how to identify various stages of age-related macular degeneration including geographic atrophy (GA) by examining optimal management scenarios implicating various ECPs and reviewing treatment considerations for patients with GA. Early identification of GA will lead to optimal patient outcomes, while a standardized management scenario will reduce clinical burden among ECPs treating patients with GA.

Potential Structural Biomarkers in 3D Images Validated by the First Functional Biomarker for Early Age-Related Macular Degeneration - ALSTAR2 Baseline

Purpose

Lack of valid end points impedes developing therapeutic strategies for early age-related macular degeneration (AMD). Delayed rod-mediated dark adaptation (RMDA) is the first functional biomarker for incident early AMD. The relationship between RMDA and the status of outer retinal bands on optical coherence tomography (OCT) have not been well defined. This study aims to characterize these relationships in early and intermediate AMD.

Methods

Baseline data from 476 participants was assessed including eyes with early AMD (n = 138), intermediate AMD (n = 101), and normal aging (n = 237). Participants underwent volume OCT imaging of the macula and rod intercept time (RIT) was measured. The ellipsoid zone (EZ) and interdigitation zone (IZ) on all OCT B-scans of the volumes were segmented. The area of detectable EZ and IZ, and mean thickness of IZ within the Early Treatment Diabetic Retinopathy Study (ETDRS) grid were computed and associations with RIT were assessed by Spearman's correlation coefficient and age adjusted.

Results

Delayed RMDA (longer RIT) was most strongly associated with less preserved IZ area (r = -0.591; P < 0.001), followed by decreased IZ thickness (r = -0.434; P < 0.001), and EZ area (r = -0.334; P < 0.001). This correlation between RIT and IZ integrity was not apparent when considering normal eyes alone within 1.5 mm of the fovea.

Conclusions

RMDA is correlated with the status of outer retinal bands in early and intermediate AMD eyes, particularly, the status of the IZ. This correlation is consistent with a previous analysis of only foveal B-scans and is biologically plausible given that retinoid availability, involving transfer at the interface attributed to the IZ, is rate-limiting for RMDA.

Structural and clinical changes in previously treated type 1 macular neovascularization in non-responder AMD eyes switched to brolucizumab

PURPOSE

To examine structural and clinical changes in previously treated type 1 macular neovascularization (MNV) in non-responder age-related macular degeneration (nAMD) eyes switched to brolucizumab. Subretinal hyper-reflective material (SHRM), intraretinal (IRF) and subretinal fluid (SRF) presence, fibrovascular-pigment epithelium detachment (PED) height and central macular thickness (CMT) variation were analyzed using optical coherence tomography (OCT).

METHODS

In this prospective study all patients underwent a complete ophthalmological evaluation including structural OCT at baseline (T0), one month (T1), three (T2), four (T3) and six months after switching to brolucizumab treatment (T4). Non-responder criterion was the persistence of IRF and SRF. Moreover, CMT and BCVA had shown worsening or no improvement before switching to brolucizumab. Clinical function and structural activity biomarkers were measured at each visit and changes were analyzed. P value <0.05 was considered statistically significant.

RESULTS

Twenty eyes of twenty patients were enrolled. All the structural variables examined during the follow-up showed significant reductions. Decreases in IRF, SRF and PED were already significant at T1 (p < 0.05). SHRM was significantly reduced at T2 (p < 0.05). Structural biomarkers were absent at T3. At T4, all biomarkers remained stable while SHRM was no longer detectable in 18 patients. Changes in visual acuity from baseline to T4 were not significant.

CONCLUSION

This short-term experience highlights that brolucizumab might be considered an effective treatment option in nAMD with type 1 MNV, as it can promote a reduction of structural activity biomarkers.

Epidemiology of geographic atrophy and its precursor features of intermediate age-related macular degeneration

Globally age-related macular degeneration (AMD) is a leading cause of blindness with a significant impact on quality of life. Geographic atrophy (GA) is the atrophic late form of AMD and its prevalence increases markedly with age with around 1 in 5 persons aged 85 and above having GA in at least one eye. Bilateral GA leads to severe visual impairment thus posing a significant burden on patients, careers and health providers. The incidence and prevalence of GA varies across different geographic regions, with the highest rates in those of European ancestry. Although heterogeneity in definitions of GA and reporting strategy can explain some of the discrepancies, the data overall are consistent in showing a lower prevalence in other ethnicities such as those of Asian heritage. This is at present unexplained but thought to be due to the existence of protective factors such as differences in eye pigmentation, diet, environmental exposures and genetic variability. This review covers key aspects of the prevalence and incidence of the ocular precursor features of GA (large drusen, pigmentary abnormalities and reticular pseudo-drusen), the late stage of GA and factors that have been known to be associated with modifying risk including systemic, demographic, environment, genetic and ocular. Understanding the global epidemiology scenario is crucial for the prevention of and management of patients with GA.

Retinal pigment epithelium-Bruch's membrane volume in grading of age-related macular degeneration

AIM

To assess the agreement of optical coherence tomography (OCT) algorithm-based retinal pigment epithelium -Bruch's membrane complex volume (RBV) with fundus photograph-based age-related macular degeneration (AMD) grading.

METHODS

Digital color fundus photographs (CFPs) and spectral domain OCT images were acquired from 96 elderly subjects. CFPs were graded according to Age-Related Eye Disease Study (AREDS) classification. OCT image segmentation and RBV data calculation were done with Orion™ software. Univariate and multivariate analyses were performed to find out whether AMD lesion features associated with higher RBVs.

RESULTS

RBV correlated with AMD grading (rs=0.338, P=0.001), the correlation was slightly stronger in early AMD (n=52; rs=0.432, P=0.001). RBV was higher in subjects with early AMD compared with those with no AMD lesions evident in fundus photographs (1.05±0.20 vs 0.96±0.13 mm3, P=0.023). In multivariate analysis higher RBVs were associated significantly with higher total drusen (β=0.388, P=0.027) and pigmentation areas (β=0.319, P=0.020) in fundus photographs, whereas depigmentation area (β=-0.295, P=0.015) associated with lower RBV.

CONCLUSION

RBV correlate with AMD grading status, with a stronger association in patients with moderate, non-late AMD grades. This effect is driven mostly by lesions with drusen or pigmentation. Lesions with depigmentation tend to have lower values. RBV is more comprehensive measurement of the key area of AMD pathogenesis, compared to sole drusen volume analysis. RBV measurements are independent on grader variations and offer a possibility to quantify early and middle grade AMD lesions in a research setting, but may not substitute fundus photograph-based grading in the whole range of AMD spectrum.

Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5)

Real-world retinal optical coherence tomography (OCT) scans are available in abundance in primary and secondary eye care centres. They contain a wealth of information to be analyzed in retrospective studies. The associated electronic health records alone are often not enough to generate a high-quality dataset for clinical, statistical, and machine learning analysis. We have developed a deep learning-based age-related macular degeneration (AMD) stage classifier, to efficiently identify the first onset of early/intermediate (iAMD), atrophic (GA), and neovascular (nAMD) stage of AMD in retrospective data. We trained a two-stage convolutional neural network to classify macula-centered 3D volumes from Topcon OCT images into 4 classes: Normal, iAMD, GA and nAMD. In the first stage, a 2D ResNet50 is trained to identify the disease categories on the individual OCT B-scans while in the second stage, four smaller models (ResNets) use the concatenated B-scan-wise output from the first stage to classify the entire OCT volume. Classification uncertainty estimates are generated with Monte-Carlo dropout at inference time. The model was trained on a real-world OCT dataset, 3765 scans of 1849 eyes, and extensively evaluated, where it reached an average ROC-AUC of 0.94 in a real-world test set.

Discernibility of the Interdigitation Zone (IZ), a Potential Optical Coherence Tomography (OCT) Biomarker for Visual Dysfunction in Aging

PURPOSE

Photoreceptor (PR) outer segments, retinal pigment epithelium apical processes, and inter-PR matrix contribute to the interdigitation zone (IZ) of optical coherence tomography (OCT). We hypothesize that this interface degrades over adulthood, in concert with a delay of rod mediated dark adaptation (RMDA). To explore this idea, we determined IZ discernibility and RMDA in younger and older adults.

METHODS

For this cross-sectional study, eyes of 20 young (20-30 years) and 40 older (≥60 years) participants with normal maculas according to the AREDS 9-step grading system underwent OCT imaging and RMDA testing at 5° superior to the fovea. Custom FIJI plugins enabled analysis for IZ discernibility at 9 eccentricities in 0.5 mm steps on one single horizontal B-scan through the fovea. Locations with discernible IZ met two criteria: visibility on B-scans and a distinct peak on a longitudinal reflectivity profile. The frequency of sites meeting both criteria was compared between both age groups and correlated with rod intercept time (RIT).

RESULTS

The median number of locations with discernible IZ was significantly higher (foveal, 4 vs. 0, p = 0.0099; extra-foveal 6 vs. 0, p < 0.001) in eyes of young (26 ± 3 years) compared to older (73 ± 5 years) participants. For the combined young and older sample, the higher frequency of discernible IZ was correlated with shorter RIT (faster dark adaptation) (rs = -0.56, p < 0.0001). This association was significant within young eyes (rs = -0.54; p = 0.0134) and not within older eyes (rs = -0.29, p = 0.706).

CONCLUSIONS

Results suggest that the interface between outer segments and apical processes degrades in normal aging, potentially contributing to delayed rod-mediated dark adaptation. More research is needed to verify an age-related association between IZ discernibility and rod-mediated dark adaptation. If confirmed in a large sample, IZ discernibility might prove to be a valuable biomarker and predictor for visual function in aging.

Anti-VEGF Drugs in Age-Related Macular Degeneration: A Focus on Dosing Regimen-Related Safety and Efficacy

Age-related macular degeneration (AMD) is one of the main causes of visual impairment and severe visual loss, and can progress to two advanced forms-neovascularization and atrophic. The field of anti-AMD drugs has undergone huge developments in recent years, from single-target intravitreal administration to current clinical studies with multi-target and non-invasive agents, offering interesting new pharmacological opportunities for the treatment of this disease. Hence, we summarize some of the approved anti-vascular endothelial growth factor (VEGF) drugs for neovascular AMD, especially their structural characteristics, clinical manifestations, dosing regimens, and safety issues of the anti-VEGF drugs highlighted. In addition, advances in atrophic AMD drug research are also briefly described.

Potential Structural Biomarkers in 3D Images Validated by the First Functional Biomarker for Early Age-Related Macular Degeneration - ALSTAR2 Baseline

Purpose

While intermediate and late age-Related Macular Degeneration (AMD) have been widely investigated, rare studies were focused on the pathophysiologic mechanism of early AMD. Delayed rod-mediated dark adaptation (RMDA) is the first functional biomarker for incident early AMD. The status of outer retinal bands on optical coherence tomography (OCT) may be potential imaging biomarkers and the purpose is to investigate the hypothesis that the integrity of interdigitation zone (IZ) may provide insight into the health of photoreceptors and retinal pigment epithelium (RPE) in early AMD.

Methods

We establish the structure-function relationship between ellipsoid zone (EZ) integrity and RMDA, and IZ integrity and RMDA in a large-scale OCT dataset from eyes with normal aging (n=237), early AMD (n=138), and intermediate AMD (n=101) by utilizing a novel deep-learning-derived algorithm with manual correction when needed to segment the EZ and IZ on OCT B-scans (57,596 B-scans), and utilizing the AdaptDx device to measure RMDA.

Results

Our data demonstrates that slower RMDA is associated with less preserved EZ (r = -0.334; p<0.001) and IZ area (r = -0.591; p<0.001), and decreased IZ thickness (r = -0.434; p<0.001). These associations are not apparent when considering normal eyes alone.

Conclusions

The association with IZ area and RMDA in large-scale data is biologically plausible because retinoid availability and transfer at the interface attributed to IZ is rate-limiting for RMDA. This study supports the hypothesis that the IZ integrity provides insight into the health of photoreceptors and RPE in early AMD and is a potential new imaging biomarker.

Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography

Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions.

Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence

INTRODUCTION

Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development.

AREAS COVERED

Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018.

EXPERT OPINION

There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD.

Age-Related Macular Degeneration: An Exponentially Emerging Imminent Threat of Visual Impairment and Irreversible Blindness

Age-related macular degeneration (AMD) is a significant cause of blindness globally. With the exponential rise in the aging population, AMD is the third leading cause of visual impairment worldwide. Neovascular AMD (nAMD; Wet AMD) and geographical atrophy (GA, late-stage dry AMD) are the advanced AMD accountable for substantial cases of visual deterioration among the elderly. Our review of the literature depicted that notable risk factors include cigarette smoking, nutritional elements, cardiovascular disorders, and genetic markers, including genes regulating complement, lipid, and angiogenic pathways. Some studies have suggested a relative decline in the proportion of AMD cases in the last two decades attributable to novel diagnostic and therapeutic modalities. Accurate diagnosis is the result of a combination of clinical examination and imaging techniques, including retinal photography, angiography, and optical coherence tomography. The incorporation of dietary antioxidant supplements, explicitly lutein, slows the progression of the disease in advanced stages. The induction of vascular endothelial growth factor (VEGF) inhibitors in the treatment of neovascular AMD, often combined with other modalities, has shown an immensely favorable prognosis. Research to integrate gene therapy and regenerative techniques using stem cells is underway to further mitigate AMD-associated morbidity. It is imperative to establish screening and therapeutic guidelines for AMD to curtail the future social and financial burden and improve the diminishing quality of life among the elderly.

Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment

Geographic atrophy (GA) represents a late stage of age-related macular degeneration, which leads to irreversible vision loss. With the first successful therapeutic approach, namely complement inhibition, huge numbers of patients will have to be monitored regularly. Given these perspectives, a strong need for automated GA segmentation has evolved. The main purpose of this study was the clinical validation of an artificial intelligence (AI)-based algorithm to segment a topographic 2D GA area on a 3D optical coherence tomography (OCT) volume, and to evaluate its potential for AI-based monitoring of GA progression under complement-targeted treatment. 100 GA patients from routine clinical care at the Medical University of Vienna for internal validation and 113 patients from the FILLY phase 2 clinical trial for external validation were included. Mean Dice Similarity Coefficient (DSC) was 0.86 ± 0.12 and 0.91 ± 0.05 for total GA area on the internal and external validation, respectively. Mean DSC for the GA growth area at month 12 on the external test set was 0.46 ± 0.16. Importantly, the automated segmentation by the algorithm corresponded to the outcome of the original FILLY trial measured manually on fundus autofluorescence. The proposed AI approach can reliably segment GA area on OCT with high accuracy. The availability of such tools represents an important step towards AI-based monitoring of GA progression under treatment on OCT for clinical management as well as regulatory trials.

Ultra-Fast Portable and Wearable Sensing Design for Continuous and Wide-Spectrum Molecular Analysis and Diagnostics

The design and characterization of spatiotemporal nano-/micro-structural arrangement that enable real-time and wide-spectrum molecular analysis is reported and demonestrated in new horizons of biomedical applications, such as wearable-spectrometry, ultra-fast and onsite biopsy-decision-making for intraoperative surgical oncology, chiral-drug identification, etc. The spatiotemporal sesning arrangement is achieved by scalable, binder-free, functionalized hybrid spin-sensitive (<↑| or <↓|) graphene-ink printed sensing layers on free-standing films made of porous, fibrous, and naturally helical cellulose networks in hierarchically stacked geometrical configuration (HSGC). The HSGC operates according to a time-space-resolved architecture that modulate the mass-transfer rate for separation, eluation and detection of each individual compound within a mixture of the like, hereby providing a mass spectrogram. The HSGC could be used for a wide range of applictions, including fast and real-time spectrogram generator of volatile organic compounds during liquid-biopsy, without the need of any immunochemistry-staining and complex power-hungry cryogenic machines; and wearable spectrometry that provide spectral signature of molecular profiles emiited from skin in the course of various dietry conditions.

Comparison of Fundus Autofluorescence Versus Optical Coherence Tomography-based Evaluation of the Therapeutic Response to Pegcetacoplan in Geographic Atrophy

PURPOSE

To perform an optical coherence tomography (OCT)-based analysis of geographic atrophy (GA) progression in patients treated with pegcetacoplan.

DESIGN

Post hoc analysis of a phase 2 multicenter, randomized, sham-controlled trial.

METHODS

Manual annotation of retinal pigment epithelium (RPE), ellipsoid zone (EZ), and external limiting membrane (ELM) loss was performed on OCT volumes from baseline and month 12 from the phase 2 FILLY trial of intravitreal pegcetacoplan for the treatment of GA secondary to age-related macular degeneration.

MAIN OUTCOME MEASURES

Correlation of GA areas measured on fundus autofluorescence and OCT. Difference in square root transformed growth rates of RPE, EZ, and ELM loss between treatment groups (monthly injection [AM], injection every other month [AEOM], and sham [SM]).

RESULTS

OCT volumes from 113 eyes of 113 patients (38 AM, 36 AEOM, and 39 SM) were included, resulting in 11 074 B-scans. The median growth of RPE loss was significantly slower in the AM group (0.158 [0.057-0.296]) than the SM group (0.255 [0.188-0.359], P = .014). Importantly, the growth of EZ loss was also significantly slower in the AM group (0.127 [0.041-0.247]) than the SM group (0.232 [0.130-0.349], P = .017). There was no significant difference in the growth of ELM loss between the treatment groups (P = .114).

CONCLUSIONS

OCT imaging provided consistent results for GA growth compared with fundus autofluorescence. In addition to slower RPE atrophy progression in patients treated with pegcetacoplan, a significant reduction in EZ impairment was also identified by OCT, suggesting the use of OCT as a potentially more sensitive monitoring tool in GA therapy.

Predicting attitudes towards screening for neurodegenerative diseases using OCT and artificial intelligence: Findings from a literature review

Recent developments in artificial intelligence (AI) and machine learning raise the possibility of screening and early diagnosis for neurodegenerative diseases, using 3D scans of the retina. The eventual value of such screening will depend not only on scientific metrics such as specificity and sensitivity but, critically, also on public attitudes and uptake. Differential screening rates for various screening programmes in England indicate that multiple factors influence uptake. In this narrative literature review, some of these potential factors are explored in relation to predicting uptake of an early screening tool for neurodegenerative diseases using AI. These include: awareness of the disease, perceived risk, social influence, the use of AI, previous screening experience, socioeconomic status, health literacy, uncontrollable mortality risk, and demographic factors. The review finds the strongest and most consistent predictors to be ethnicity, social influence, the use of AI, and previous screening experience. Furthermore, it is likely that factors also interact to predict the uptake of such a tool. However, further experimental work is needed both to validate these predictions and explore interactions between the significant predictors.

The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications.

Predicting Visual Acuity in Patients Treated for AMD

The leading diagnostic tool in modern ophthalmology, Optical Coherence Tomography (OCT), is not yet able to establish the evolution of retinal diseases. Our task is to forecast the progression of retinal diseases by means of machine learning technologies. The aim is to help the ophthalmologist to determine when early treatment is needed in order to prevent severe vision impairment or even blindness. The acquired data are made up of sequences of visits from multiple patients with age-related macular degeneration (AMD), which, if not treated at the appropriate time, may result in irreversible blindness. The dataset contains 94 patients with AMD and there are 161 eyes included with more than one medical examination. We used various techniques from machine learning (linear regression, gradient boosting, random forest and extremely randomised trees, bidirectional recurrent neural network, LSTM network, GRU network) to handle technical challenges such as how to learn from small-sized time series, how to handle different time intervals between visits, and how to learn from different numbers of visits for each patient (1–5 visits). For predicting the visual acuity, we performed several experiments with different features. First, by considering only previous measured visual acuity, the best accuracy of 0.96 was obtained based on a linear regression. Second, by considering numerical OCT features such as previous thickness and volume values in all retinal zones, the LSTM network reached the highest score (R2=0.99). Third, by considering the fundus scan images represented as embeddings obtained from the convolutional autoencoder, the accuracy was increased for all algorithms. The best forecasting results for visual acuity depend on the number of visits and features used for predictions, i.e., 0.99 for LSTM based on three visits (monthly resampled series) based on numerical OCT values, fundus images, and previous visual acuities.

Genetic Association Analysis of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration

Anti-VEGF treatment for neovascular age-related macular degeneration (nAMD) has been FDA-approved in 2004, and since then has helped tens of thousands of patients worldwide to preserve vision. Still, treatment responses vary widely, emphasizing the need for genetic biomarkers to robustly separate responders from non-responders. Here, we report the findings of an observational study compromising 179 treatment-naïve nAMD patients and their reaction to treatment after three monthly doses of anti-VEGF antibodies. We show that established criteria of treatment response such as visual acuity and central retinal thickness successfully divides our cohort into 128 responders and 51 non-responders. Nevertheless, retinal thickness around the fovea revealed significant reaction to treatment even in the formally categorized non-responders. To elucidate genetic effects underlying our criteria, we conducted an undirected genome-wide association study followed by a directed replication study of 30 previously reported genetic variants. Remarkably, both approaches failed to result in significant findings, suggesting study-specific effects were confounding the present and previous discovery studies. Of note, all studies so far are greatly underpowered, hampering interpretation of genetic findings. In consequence, we highlight the need for an extensive phenotyping study with sample sizes exceeding at least 15,000 to reliably assess anti-VEGF treatment responses in nAMD.

Efficacy Outcomes of Brolucizumab Versus Aflibercept in Neovascular Age-Related Macular Degeneration Patients with Early Residual Fluid

OBJECTIVE

To compare the outcomes of brolucizumab versus aflibercept in patients with neovascular age-related macular degeneration with early residual fluid in the HAWK and HARRIER studies.

DESIGN

Post hoc analysis using pooled data from the phase 3 studies HAWK (NCT02307682) and HARRIER (NCT02434328).

PARTICIPANTS

The early residual fluid cohort for this post hoc analysis consisted of patients treated with either brolucizumab 6 mg (n = 730 patients) or aflibercept 2 mg (n = 729 patients) and who had the presence of intraretinal fluid (IRF), subretinal fluid (SRF), or both verified by spectral-domain OCT at the week 12 clinic visit.

METHODS

After 3 initial monthly doses, patients treated with brolucizumab received injections every 12 weeks (q12w) or every 8 weeks (q8w), depending on the neovascular age-related macular degeneration disease activity, whereas patients treated with aflibercept received fixed q8w dosing.

MAIN OUTCOME MEASURES

The mean change in best-corrected visual acuity and central subfield thickness (CST) from the baseline to that at weeks 48 and 96, the proportion of patients treated with brolucizumab remaining on q12w dosing to week 96, and the change in fluid status at weeks 48 and 96.

RESULTS

All analyses were based on 149 of 730 (20.4%) patients treated with brolucizumab and 217 of 729 (29.8%) patients treated with aflibercept with spectral-domain OCT-verified IRF, SRF, or both at their week 12 visit. The best-corrected visual acuity improvements from baseline at weeks 48 and 96 were numerically better for brolucizumab than for aflibercept (least square mean [standard error] 7.9 ± 1.1 vs. 4.6 ± 0.9 and 7.4 ± 1.3 vs. 2.9 ± 1.1 letters, respectively). The CST reductions from baseline at weeks 48 and 96 were consistently greater with brolucizumab than with aflibercept (least square mean [standard error], μm: -194.9 ± 13.7 vs. -123.9 ± 11.3; and -201.1 ± 14.5 vs. -134.2 ± 12.0, respectively). At weeks 48 and 96, patients treated with brolucizumab had a 40.4% and 31.3% probability of remaining on q12w dosing intervals, respectively. Fewer patients treated with brolucizumab had remaining IRF, SRF, or both at weeks 48 and 96 than patients treated with aflibercept (59.1% vs. 75.1% and 49.0% vs. 60.4%, respectively).

CONCLUSIONS

In patients with early residual fluid, defined as spectral-domain OCT-verified IRF, SRF, or both at the week 12 clinic visit, brolucizumab resolved the early residual fluid and reduced CST more effectively than aflibercept, resulting in greater best-corrected visual acuity improvements through week 96 of anti-VEGF treatment.

Intraoperative imaging in pathology-assisted surgery

The pathological assessment of surgical specimens during surgery can reduce the incidence of positive resection margins, which otherwise can result in additional surgeries or aggressive therapeutic regimens. To improve patient outcomes, intraoperative spectroscopic, fluorescence-based, structural, optoacoustic and radiological imaging techniques are being tested on freshly excised tissue. The specific clinical setting and tumour type largely determine whether endogenous or exogenous contrast is to be detected and whether the tumour specificity of the detected biomarker, image resolution, image-acquisition times or penetration depth are to be prioritized. In this Perspective, we describe current clinical standards for intraoperative tissue analysis and discuss how intraoperative imaging is being implemented. We also discuss potential implementations of intraoperative pathology-assisted surgery for clinical decision-making.

SD-OCT Biomarkers and the Current Status of Artificial Intelligence in Predicting Progression from Intermediate to Advanced AMD

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the Western World. Optical coherence tomography (OCT) has revolutionized the diagnosis and follow-up of AMD patients. This review focuses on SD-OCT imaging biomarkers which were identified as predictors for progression in intermediate AMD to late AMD, either geographic atrophy (GA) or choroidal neovascularization (CNV). Structural OCT remains the most compelling modality to study AMD features related to the progression such as drusen characteristics, hyperreflective foci (HRF), reticular pseudo-drusen (RPD), sub-RPE hyper-reflective columns and their impact on retinal layers. Further on, we reviewed articles that attempted to integrate biomarkers that have already proven their involvement in intermediate AMD progression, in their models of artificial intelligence (AI). By combining structural biomarkers with genetic risk and lifestyle the predictive ability becomes more accurate.

Intersession Repeatability of Structural Biomarkers in Early and Intermediate Age-Related Macular Degeneration: A MACUSTAR Study Report

Purpose

To analyze the intersession repeatability of structural biomarkers in eyes with early and intermediate age-related macular degeneration (iAMD) within the cross-sectional part of the observational multicenter MACUSTAR study.

Methods

Certified site personnel obtained multimodal imaging data at two visits (38 ± 20 [mean ± standard deviation] days apart), including spectral-domain optical coherence tomography (SD-OCT). One junior reader performed systematic and blinded grading at the central reading center, followed by senior reader review. Structural biomarkers included maximum drusen size classification (>63 to ≤125 µm vs. >125 µm), presence of large pigment epithelium detachments (PEDs), reticular pseudodrusen (RPD), vitelliform lesions, and refractile deposits. Intrasession variability was assessed using Cohen's κ statistics.

Results

At the first visit, 202 study eyes of 202 participants were graded as manifesting with either early (n = 34) or intermediate (n = 168) AMD. Grading of imaging data between visits revealed perfect agreement for the maximum drusen size classification (κ = 0.817; 95% confidence interval, 0.70–0.94). In iAMD eyes, perfect to substantial agreement was determined for the presence of large PEDs (0.87; 0.69–1.00) and RPD (0.752; 0.63–0.87), while intersession agreement was lower for the presence of vitelliform lesions (0.649; 0.39–0.65) and refractile deposits (0.342; −0.029–0.713), respectively.

Conclusions

Multimodal retinal imaging analysis between sessions showed a higher repeatability for structural biomarkers with predefined cutoff values than purely qualitative defined parameters.

Translational Relevance

A high repeatability of retinal imaging biomarkers will be important to implement automatic grading approaches and to establish robust and meaningful structural clinical endpoints for future interventional clinical trials in patients with iAMD.

Performance of a Machine-Learning Computational Image Analysis Algorithm in Retinal Fluid Quantification for Patients With Diabetic Macular Edema and Retinal Vein Occlusions

BACKGROUND AND OBJECTIVE

The objective is to validate an automated artificial intelligence model in detecting and quantifying fluid in diabetic macular edema (DME) and retinal vein occlusion (RVO) optical coherence tomography images.

PATIENTS AND METHODS

DME (n = 100) and RVO (n = 100) images of adult patients were reviewed. The performance of machine-learning (ML) computational image analysis algorithm was evaluated against consensus manual grading. Main outcomes were accuracy and sensitivity for detection and Pearson's correlation coefficients for quantification.

RESULTS

The ML algorithm had a high accuracy and sensitivity in both DME (intraretinal fluid [IRF]: 0.92, 0.97; subretinal fluid [SRF]: 0.93, 1.00) and RVO (IRF: 0.94, 0.99; SRF: 0.93, 1.00). It had moderate-high correlation in quantifying fluid in DME (total retinal fluid: 0.88; IRF: 0.88; SRF: 0.97) and RVO (total retinal fluid: 0.83; IRF: 0.76; SRF: 0.64).

CONCLUSION

The ML algorithm is highly accurate and sensitive in detecting fluid in DME and RVO optical coherence tomography images and effectively quantifies IRF and SRF in both disease states, particularly in images with low to moderate fluid burden. [Ophthalmic Surg Lasers Imaging Retina. 2022;53:123-131.].

Clinical applications for intraoperative optical coherence tomography: a systematic review

In this systematic review, we provide an overview of the current state of intraoperative optical coherence tomography (iOCT). As iOCT technology is increasingly utilized, its current clinical applications and potential uses warrant attention. Here, we categorize the findings of various studies by their respective fields, including the use of iOCT in vitreoretinal surgery, corneal surgery, glaucoma surgery, cataract surgery, and pediatric ophthalmology. The trend observed in recent decades towards performing minimally invasive ophthalmic surgery has caused practitioners to recognize the limitations of using a conventional surgical microscope for intraoperative visualization. Thus, the superior visualization provided by iOCT can improve the safety of these surgical techniques and promote the development of new minimally invasive ophthalmic surgeries. Landmark prospective studies found that iOCT can significantly affect surgical decision making and can cause a subsequent change in surgical strategy, and the use of iOCT has potential to improve surgical outcome. Despite these advantages, however, iOCT is still a relatively new technique, and beginning users of iOCT can encounter limitations that can preclude their reaching the full potential of iOCT and in this respect several improvements are needed.

Quantitative assessment of retinal fluid in neovascular age-related macular degeneration under anti-VEGF therapy

The retinal world has been revolutionized by optical coherence tomography (OCT) and anti-vascular endothelial growth factor (VEGF) therapy. The numbers of intravitreal injections are on a constant rise and management in neovascular age-related macular degeneration (nAMD) is mainly driven by the qualitative assessment of macular fluid as detected on OCT scans. The presence of macular fluid, particularly subretinal fluid (SRF) and intraretinal fluid (IRF), has been used to trigger re-treatments in clinical trials and the real world. However, large discrepancies can be found between the evaluations of different readers or experts and especially small amounts of macular fluid might be missed during this process. Pixel-wise detection of macular fluid uses an entire OCT volume to calculate exact volumes of retinal fluid. While manual annotations of such pixel-wise fluid detection are unfeasible in a clinical setting, artificial intelligence (AI) is able to overcome this hurdle by providing real-time results of macular fluid in different retinal compartments. Quantitative fluid assessments have been used for various post hoc analyses of randomized controlled trials, providing novel insights into anti-VEGF treatment regimens. Nonetheless, the application of AI-algorithms in a prospective patient care setting is still limited. In this review, we discuss the use of quantitative fluid assessment in nAMD during anti-VEGF therapy and provide an outlook to novel forms of patient care with the support of AI quantifications.

Age-related macular degeneration: Epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy

Age-related macular degeneration (AMD) is a complex eye disorder and is the leading cause of incurable blindness worldwide in the elderly. Clinically, AMD initially affects the central area of retina known as the macula and it is classified as early stage to late stage (advanced AMD). The advanced AMD is classified into the nonexudative or atrophic form (dry AMD) and the exudative or neovascular form (wet AMD). More severe vision loss is typically associated with the wet form. Multiple genetic factors, lipid metabolism, oxidative stress and aging, play a role in the etiology of AMD. Dysregulation in genetic to AMD is established to 46%-71% of disease contribution, with CFH and ARMS2/HTRA1 to be the two most notable risk loci among the 103 identified AMD associated loci so far. Chronic cigarette smoking is the most proven consistently risk living habits for AMD. Deep learning algorithm has been developed based on image recognition to distinguish wet AMD and normal macula with high accuracy. Currently, anti-vascular endothelial growth factor (VEGF) therapy is highly effective at treating wet AMD. Several new generation AMD drugs and iPSC-derived RPE cell therapy are in the clinical trial stage and are promising to improve AMD treatment in the near future.

Retinal Progression Biomarkers of Early and Intermediate Age-Related Macular Degeneration

Early and intermediate AMD patients represent a heterogeneous population with an important but variable risk of progression to more advanced stages of the disease. The five-year progression from early and intermediate AMD to late disease is known to range from 0.4% to 53%. This wide variation explains the particular interest in searching predictive AMD biomarkers. Clinical parameters such as drusen size, presence of pigmentary abnormalities, and fellow eye status were, traditionally, the more important predictive elements. Multimodal retinal assessment (Color Fundus Photography, Optical Coherence Tomography, Optical Coherence Angiography and Fundus Autofluorescence) is providing new and accurate image biomarkers, useful in research and in daily practice. If individual progression risk could be anticipated, then management plans should be adapted accordingly, considering follow-up intervals and therapeutic interventions. Here, we reviewed the most important image progression biomarkers of early and intermediate AMD with relevant interest in clinical practice.

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging

Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.

Robotically aligned optical coherence tomography with 5 degree of freedom eye tracking for subject motion and gaze compensation

Optical coherence tomography (OCT) has revolutionized diagnostics in ophthalmology. However, OCT requires a trained operator and patient cooperation to carefully align a scanner with the subject's eye and orient it in such a way that it images a desired region of interest at the retina. With the goal of automating this process of orienting and aligning the scanner, we developed a robot-mounted OCT scanner that automatically aligned with the pupil while matching its optical axis with the target region of interest at the retina. The system used two 3D cameras for face tracking and three high-resolution 2D cameras for pupil and gaze tracking. The tracking software identified 5 degrees of freedom for robot alignment and ray aiming through the ocular pupil: 3 degrees of translation (x, y, z) and 2 degrees of orientation (yaw, pitch). We evaluated the accuracy, precision, and range of our tracking system and demonstrated imaging performance on free-standing human subjects. Our results demonstrate that the system stabilized images and that the addition of gaze tracking and aiming allowed for region-of-interest specific alignment at any gaze orientation within a 28° range.

Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine

The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.

Longitudinal changes in the ganglion cell-inner plexiform layer thickness of age-related macular degeneration

PURPOSE

To determine longitudinal changes of the ganglion cell-inner plexiform layer (GC-IPL) thickness in patients with non-exudative age-related macular degeneration (AMD) without other ophthalmic disease.

METHODS

Thirty-three eyes of 33 patients with early and intermediate non-exudative AMD (non-exudative AMD group) and 33 normal control eyes were followed for 2 years, and GC-IPL thickness was measured by spectral domain optical coherence tomography at 1-year intervals. The mean rate of GC-IPL reduction was estimated using a linear mixed model and compared between two groups.

RESULTS

The mean age of patients in the non-exudative AMD group and control groups were 68.82 ± 6.81 years and 67.73 ± 5.87 years, respectively (p = 0.488). The mean GC-IPL thickness at the first visit was 76.61 ± 16.33 μm in the non-exudative AMD and 81.76 ± 3.69 μm in control group (p = 0.387), and these values significantly decreased over time, with an average reduction rate of average GC-IPL -0.86 μm/year in the non-exudative AMD group and -0.32 μm/year in the control group. The difference between two groups was statistically significant (p < 0.001), and there was also a significant interaction between group and duration in linear mixed models in mean GC-IPL thickness (p = 0.001).

CONCLUSIONS

The reduction rate of the GC-IPL thickness was greater in non-exudative AMD eyes, even at relatively early stages of the disease. Physicians should maintain awareness of the presence of non-exudative AMD in various cases of ophthalmic diseases where GC-IPL thickness evaluation is necessary.

Outer Retinal Layer Thickening Predicts the Onset of Exudative Neovascular Age-Related Macular Degeneration

PURPOSE

To assess changes in outer retinal layer (ORL) thickness before the development of exudative macular neovascularization (MNV) in eyes with age-related macular degeneration.

DESIGN

Retrospective observational case series.

METHODS

Eyes with age-related macular degeneration that eventually developed exudative MNV followed with sequential optical coherence tomography for ≥2 years before the exudation occurred were enrolled. The ORL thickness was automatically calculated by the optical coherence tomography software for each sector of the early treatment diabetic retinopathy study map at each follow-up visit. The ORL thickness change from baseline to the day when the exudative MNV developed was compared between sectors that eventually developed exudative MNV and those that did not.

RESULTS

Forty-seven eyes (47 patients) were included. At baseline (24 ± 3 months before exudative MNV), mean (standard deviation) ORL thickness of sectors that eventually developed exudative MNV was similar to that of sectors that did not (85.2 [8.2] µm vs 86.8 [5.7] µm, P = .08). ORL thickness significantly increased in sectors that developed exudative MNV compared with those that did not (+5.8 [10.4] µm vs -2.8 [3.6] µm, P < .01). The regression model based on these data predicted an increase in ORL thickness from baseline of +4.2% 55 days and +11.1% 30 days before exudative MNV was detected. The ORL thickness of areas that did not develop exudative MNV did not change.

CONCLUSION

Thickening of the ORL begins in the area where exudative MNV will develop long before the exudation, accelerating significantly in the last 2 months. The occurrence of exudative MNV could be predicted by 2 months using this simple analysis.

TOPOGRAPHIC ANALYSIS OF PHOTORECEPTOR LOSS CORRELATED WITH DISEASE MORPHOLOGY IN NEOVASCULAR AGE-RELATED MACULAR DEGENERATION

PURPOSE

To quantify morphologic photoreceptor integrity during anti-vascular endothelial growth factor (anti-VEGF) therapy of neovascular age-related macular degeneration and correlate these findings with disease morphology and function.

METHODS

This presents a post hoc analysis on spectral-domain optical coherence tomography data of 185 patients, acquired at baseline, Month 3, and Month 12 in a multicenter, prospective trial. Loss of the ellipsoid zone (EZ) was manually quantified in all optical coherence tomography volumes. Intraretinal cystoid fluid, subretinal fluid (SRF), and pigment epithelial detachments were automatically segmented in the full volumes using validated deep learning methods. Spatiotemporal correlation of fluid markers with EZ integrity as well as bivariate analysis between EZ integrity and best-corrected visual acuity was performed.

RESULTS

At baseline, EZ integrity was predominantly impaired in the fovea, showing progressive recovery during anti-vascular endothelial growth factor therapy. Topographic analysis at baseline revealed EZ integrity to be more likely intact in areas with SRF and vice versa. Moreover, we observed a correlation between EZ integrity and resolution of SRF. Foveal EZ integrity correlated with best-corrected visual acuity at all timepoints.

CONCLUSION

Improvement of EZ integrity during anti-VEGF therapy of neovascular age-related macular degeneration occurred predominantly in the fovea. Photoreceptor integrity correlated with best-corrected visual acuity. Ellipsoid zone integrity was preserved in areas of SRF and showed deterioration upon SRF resolution.

Prevention of the Onset of Age-Related Macular Degeneration

Age-related macular degeneration (AMD) represents the leading cause of irreversible blindness in elderly people, mostly after the age of 65. The progressive deterioration of visual function in patients affected by AMD has a significant impact on quality of life and has also high social costs. The current therapeutic options are only partially able to slow down the natural course of the disease, without being capable of stopping its progression. Therefore, better understanding of the possibilities to prevent the onset of the disease is needed. In this regard, a central role is played by the identification of risk factors, which might participate to the development of the disease. Among these, the most researched are dietary risk factors, lifestyle, and light exposure. Many studies showed that a higher dietary intake of nutrients, such as lutein, zeaxanthin, beta carotene, omega-3 fatty acids and zinc, reduced the risk of early AMD. Regarding lifestyle habits, the association between smoking and AMD is currently accepted. Finally, retinal damage caused by ultraviolet rays and blue light is also worthy of attention. The scope of this review is to summarize the present knowledge focusing on the measures to adopt in order to prevent the onset of AMD.

OCT Biomarkers in Neovascular Age-Related Macular Degeneration: A Narrative Review

Age-related macular degeneration (AMD) is the leading cause of legal blindness in elderly people. Neovascular AMD (nAMD) is responsible for the majority of cases of severe visual loss in eyes with AMD. Optical coherence tomography (OCT) is the most widely used technology for the diagnosis and follow-up of nAMD patients, which is widely used to study and guide the clinical approach, as well as to predict and evaluate treatment response. The aim of this review is to describe and analyze various structural OCT-based biomarkers, which have practical value during both initial assessment and treatment follow-up of nAMD patients. While central retinal thickness has been the most common and one of the first OCT identified biomarkers, today, other qualitative and quantitative biomarkers provide novel insight into disease activity and offer superior prognostic value and better guidance for tailored therapeutic management. The key importance of retinal fluid compartmentalization (intraretinal fluid, subretinal fluid, and subretinal pigment epithelium (RPE) fluid) will be discussed firstly. In the second part, the structural alterations of different retinal layers in various stages of the disease (photoreceptors layer integrity, hyperreflective dots, outer retinal tubulations, subretinal hyperreflective material, and retinal pigment epithelial tears) will be analyzed in detail. The last part of the review will focus on how alterations of the vitreoretinal interface (vitreomacular adhesion and traction) and of the choroid (sub-RPE hyperreflective columns, prechoroidal clefts, choroidal caverns, choroidal thickness and choroidal volume, and choroidal vascular index) interact with nAMD progression. OCT technology is evolving very quickly, and new retinal biomarkers are continuously described. This up-to-date review article provides a comprehensive description on how structural OCT-based biomarkers provide a valuable tool to monitor the progression of the disease and the treatment response in nAMD patients. Thus, in this perspective, clinicians will be able to allocate hospital resources in the best possible way and tailor treatment to the individual patient's needs.

A review of low-cost and portable optical coherence tomography

Optical coherence tomography (OCT) is a powerful optical imaging technique capable of visualizing the internal structure of biological tissues at near cellular resolution. For years, OCT has been regarded as the standard of care in ophthalmology, acting as an invaluable tool for the assessment of retinal pathology. However, the costly nature of most current commercial OCT systems has limited its general accessibility, especially in low-resource environments. It is therefore timely to review the development of low-cost OCT systems as a route for applying this technology to population-scale disease screening. Low-cost, portable and easy to use OCT systems will be essential to facilitate widespread use at point of care settings while ensuring that they offer the necessary imaging performances needed for clinical detection of retinal pathology. The development of low-cost OCT also offers the potential to enable application in fields outside ophthalmology by lowering the barrier to entry. In this paper, we review the current development and applications of low-cost, portable and handheld OCT in both translational and research settings. Design and cost-reduction techniques are described for general low-cost OCT systems, including considerations regarding spectrometer-based detection, scanning optics, system control, signal processing, and the role of 3D printing technology. Lastly, a review of clinical applications enabled by low-cost OCT is presented, along with a detailed discussion of current limitations and outlook.

Contactless optical coherence tomography of the eyes of freestanding individuals with a robotic scanner

Clinical systems for optical coherence tomography (OCT) are used routinely to diagnose and monitor patients with a range of ocular diseases. They are large tabletop instruments operated by trained staff, and require mechanical stabilization of the head of the patient for positioning and motion reduction. Here we report the development and performance of a robot-mounted OCT scanner for the autonomous contactless imaging, at safe distances, of the eyes of freestanding individuals without the need for operator intervention or head stabilization. The scanner uses robotic positioning to align itself with the eye to be imaged, as well as optical active scanning to locate the pupil and to attenuate physiological eye motion. We show that the scanner enables the acquisition of OCT volumetric datasets, comparable in quality to those of clinical tabletop systems, that resolve key anatomic structures relevant for the management of common eye conditions. Robotic OCT scanners may enable the diagnosis and monitoring of patients with eye conditions in non-specialist clinics.

Automated Quantification of Pathological Fluids in Neovascular Age-Related Macular Degeneration, and Its Repeatability Using Deep Learning

Purpose

To develop a reliable algorithm for the automated identification, localization, and volume measurement of exudative manifestations in neovascular age-related macular degeneration (nAMD), including intraretinal (IRF), subretinal fluid (SRF), and pigment epithelium detachment (PED), using a deep-learning approach.

Methods

One hundred seven spectral domain optical coherence tomography (OCT) cube volumes were extracted from nAMD eyes. Manual annotation of IRF, SRF, and PED was performed. Ninety-two OCT volumes served as training and validation set, and 15 OCT volumes from different patients as test set. The performance of our fluid segmentation method was quantified by means of pixel-wise metrics and volume correlations and compared to other methods. Repeatability was tested on 42 other eyes with five OCT volume scans acquired on the same day.

Results

The fully automated algorithm achieved good performance for the detection of IRF, SRF, and PED. The area under the curve for detection, sensitivity, and specificity was 0.97, 0.95, and 0.99, respectively. The correlation coefficients for the fluid volumes were 0.99, 0.99, and 0.91, respectively. The Dice score was 0.73, 0.67, and 0.82, respectively. For the largest volume quartiles the Dice scores were >0.90. Including retinal layer segmentation contributed positively to the performance. The repeatability of volume prediction showed a standard deviations of 4.0 nL, 3.5 nL, and 20.0 nL for IRF, SRF, and PED, respectively.

Conclusions

The deep-learning algorithm can simultaneously acquire a high level of performance for the identification and volume measurements of IRF, SRF, and PED in nAMD, providing accurate and repeatable predictions. Including layer segmentation during training and squeeze-excite block in the network architecture were shown to boost the performance.

Translational Relevance

Potential applications include measurements of specific fluid compartments with high reproducibility, assistance in treatment decisions, and the diagnostic or scientific evaluation of relevant subgroups.

AI-based monitoring of retinal fluid in disease activity and under therapy

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Reproducible measurement of retinal fluid relies on precise AI-based segmentation methods that assign a label to each OCT voxel denoting its fluid type such as intraretinal fluid (IRF) and subretinal fluid (SRF) or pigment epithelial detachment (PED) and its location within the central 1-, 3- and 6-mm macular area. Such reliable analysis is most relevant to reflect differences in pathophysiological mechanisms and impacts on retinal function, and the dynamics of fluid resolution during therapy with different regimens and substances. Yet, an in-depth understanding of the mode of action of supervised and unsupervised learning, the functionality of a convolutional neural net (CNN) and various network architectures is needed. Greater insight regarding adequate methods for performance, validation assessment, and device- and scanning-pattern-dependent variations is necessary to empower ophthalmologists to become qualified AI users. Fluid/function correlation can lead to a better definition of valid fluid variables relevant for optimal outcomes on an individual and a population level. AI-based fluid analysis opens the way for precision medicine in real-world practice of the leading retinal diseases of modern times.

Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning

PURPOSE

We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD).

DESIGN

Development and validation of a deep-learning model for feature segmentation.

METHODS

Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve.

RESULTS

On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers.

CONCLUSIONS

The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.