Critical Dependence on Area in Relationship between ARMS2/HTRA1 Genotype and Faster Geographic Atrophy Enlargement: Age-Related Eye Disease Study 2 Report Number 33.

PURPOSE: To analyze ARMS2/HTRA1 as a risk factor for faster geographic atrophy (GA) enlargement according to (1) GA area and (2) contiguous enlargement versus progression to multifocality. DESIGN: Age-Related Eye Disease Study 2 (AREDS2) cohort analysis. PARTICIPANTS: Eyes with GA: 546 eyes of 406 participants. METHODS: Geographic atrophy area was measured from color fundus photographs at annual visits. Mixed-model regression of square root of GA area and proportional hazards regression of progression to multifocality were analyzed by ARMS2 genotype. MAIN OUTCOME MEASURES: Change in square root GA area and progression to multifocality. RESULTS: Geographic atrophy enlargement was significantly faster with ARMS2 risk alleles (P < 0.0001) at 0.224 mm/year (95% CI, 0.195-0.252 mm/year), 0.298 mm/year (95% CI, 0.271-0.324 mm/year), and 0.317 mm/year (95% CI, 0.279-0.355 mm/year), for 0 to 2 risk alleles, respectively. However, a significant interaction (P = 0.011) was observed between genotype and baseline area. In eyes with very small area (< 1.9 mm2), enlargement was significantly faster with ARMS2 risk alleles (P < 0.0001) at 0.193 mm/year (95% CI, 0.162-0.225 mm/year) versus 0.304 mm/year (95% CI, 0.280-0.329 mm/year) for 0 versus 1 to 2 risk alleles, respectively. With moderately small (1.9-3.8 mm2) or medium to large (≥ 3.8 mm2) area, enlargement was not significantly faster with ARMS2 risk alleles (P = 0.66 and P = 0.70, respectively). In nonmultifocal GA, enlargement was significantly faster with ARMS2 risk alleles (P = 0.001) at 0.175 mm/year (95% CI, 0.142-0.209 mm/year), 0.226 mm/year (95% CI, 0.193-0.259 mm/year), and 0.287 mm/year (95% CI, 0.237-0.337 mm/year) with 0 to 2 risk alleles, respectively. ARMS2 genotype was not associated significantly with progression to multifocal GA. CONCLUSIONS: The relationship between ARMS2/HTRA1 genotype and faster GA enlargement depends critically on GA area: risk alleles represent a strong risk factor for faster enlargement only in eyes with very small area. They increase the growth rate more through contiguous enlargement than progression to multifocality. ARMS2/HTRA1 genotype is more important in increasing risk of progression to GA and initial GA enlargement (contiguously) than in subsequent enlargement or progression to multifocality. These findings may explain some discrepancies between previous studies and have implications for both research and clinical practice. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

An Updated Simplified Severity Scale for Age-Related Macular Degeneration Incorporating Reticular Pseudodrusen: Age-Related Eye Disease Study Report Number 42.

PURPOSE: To update the Age-Related Eye Disease Study (AREDS) simplified severity scale for risk of late age-related macular degeneration (AMD), including incorporation of reticular pseudodrusen (RPD), and to perform external validation on the Age-Related Eye Disease Study 2 (AREDS2). DESIGN: Post hoc analysis of 2 clinical trial cohorts: AREDS and AREDS2. PARTICIPANTS: Participants with no late AMD in either eye at baseline in AREDS (n = 2719) and AREDS2 (n = 1472). METHODS: Five-year rates of progression to late AMD were calculated according to levels 0 to 4 on the simplified severity scale after 2 updates: (1) noncentral geographic atrophy (GA) considered part of the outcome, rather than a risk feature, and (2) scale separation according to RPD status (determined by validated deep learning grading of color fundus photographs). MAIN OUTCOME MEASURES: Five-year rate of progression to late AMD (defined as neovascular AMD or any GA). RESULTS: In the AREDS, after the first scale update, the 5-year rates of progression to late AMD for levels 0 to 4 were 0.3%, 4.5%, 12.9%, 32.2%, and 55.6%, respectively. As the final simplified severity scale, the 5-year progression rates for levels 0 to 4 were 0.3%, 4.3%, 11.6%, 26.7%, and 50.0%, respectively, for participants without RPD at baseline and 2.8%, 8.0%, 29.0%, 58.7%, and 72.2%, respectively, for participants with RPD at baseline. In external validation on the AREDS2, for levels 2 to 4, the progression rates were similar: 15.0%, 27.7%, and 45.7% (RPD absent) and 26.2%, 46.0%, and 73.0% (RPD present), respectively. CONCLUSIONS: The AREDS AMD simplified severity scale has been modernized with 2 important updates. The new scale for individuals without RPD has 5-year progression rates of approximately 0.5%, 4%, 12%, 25%, and 50%, such that the rates on the original scale remain accurate. The new scale for individuals with RPD has 5-year progression rates of approximately 3%, 8%, 30%, 60%, and 70%, that is, approximately double for most levels. This scale fits updated definitions of late AMD, has increased prognostic accuracy, seems generalizable to similar populations, but remains simple for broad risk categorization. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Reticular Pseudodrusen Status, ARMS2/HTRA1 Genotype, and Geographic Atrophy Enlargement: Age-Related Eye Disease Study 2 Report 32.

PURPOSE: To determine whether reticular pseudodrusen (RPD) status, ARMS2/HTRA1 genotype, or both are associated with altered geographic atrophy (GA) enlargement rate and to analyze potential mediation of genetic effects by RPD status. DESIGN: Post hoc analysis of an Age-Related Eye Disease Study 2 cohort. PARTICIPANTS: Eyes with GA: n = 771 from 563 participants. METHODS: Geographic atrophy area was measured from fundus photographs at annual visits. Reticular pseudodrusen presence was graded from fundus autofluorescence images. Mixed-model regression of square root of GA area was performed by RPD status, ARMS2 genotype, or both. MAIN OUTCOME MEASURES: Change in square root of GA area. RESULTS: Geographic atrophy enlargement was significantly faster in eyes with RPD (P < 0.0001): 0.379 mm/year (95% confidence interval [CI], 0.329-0.430 mm/year) versus 0.273 mm/year (95% CI, 0.256-0.289 mm/year). Enlargement was also significantly faster in individuals carrying ARMS2 risk alleles (P < 0.0001): 0.224 mm/year (95% CI, 0.198-0.250 mm/year), 0.287 mm/year (95% CI, 0.263-0.310 mm/year), and 0.307 mm/year (95% CI, 0.273-0.341 mm/year) for 0, 1, and 2, respectively. In mediation analysis, the direct effect of ARMS2 genotype was 0.074 mm/year (95% CI, 0.009-0.139 mm/year), whereas the indirect effect of ARMS2 genotype via RPD status was 0.002 mm/year (95% CI, -0.006 to 0.009 mm/year). In eyes with incident GA, RPD presence was not associated with an altered likelihood of central involvement (P = 0.29) or multifocality (P = 0.16) at incidence. In eyes with incident noncentral GA, RPD presence was associated with faster GA progression to the central macula (P = 0.009): 157 µm/year (95% CI, 126-188 µm/year) versus 111 µm/year (95% CI, 97-125 µm/year). Similar findings were observed in the Age-Related Eye Disease Study. CONCLUSIONS: Geographic atrophy enlargement is faster in eyes with RPD and in individuals carrying ARMS2/HTRA1 risk alleles. However, RPD status does not mediate the association between ARMS2/HTRA1 genotype and faster enlargement. Reticular pseudodrusen presence and ARMS2/HTRA1 genotype are relatively independent risk factors, operating by distinct mechanisms. Reticular pseudodrusen presence does not predict central involvement or multifocality at GA incidence but is associated with faster progression toward the central macula. Reticular pseudodrusen status should be considered for improved predictions of enlargement rate. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Artificial intelligence for home monitoring devices.

PURPOSE OF REVIEW: Home monitoring in ophthalmology is appropriate for disease stages requiring frequent monitoring or rapid intervention, for example, neovascular age-related macular degeneration (AMD) and glaucoma, where the balance between frequent hospital attendance versus risk of late detection is a constant challenge. Artificial intelligence approaches are well suited to address some challenges of home monitoring. RECENT FINDINGS: Ophthalmic data collected at home have included functional (e.g. perimetry), biometric (e.g. intraocular pressure), and imaging [e.g. optical coherence tomography (OCT)] data. Potential advantages include early detection/intervention, convenience, cost, and visual outcomes. Artificial intelligence can assist with home monitoring workflows by handling large data volumes from frequent testing, compensating for test quality, and extracting useful metrics from complex data. Important use cases include machine learning applied to hyperacuity self-testing for detecting neovascular AMD and deep learning applied to OCT data for quantifying retinal fluid. SUMMARY: Home monitoring of health conditions is useful for chronic diseases requiring rapid intervention or frequent data sampling to decrease risk of irreversible vision loss. Artificial intelligence may facilitate accurate, frequent, large-scale home monitoring, if algorithms are integrated safely into workflows. Clinical trials and economic evaluations are important to demonstrate the value of artificial intelligence-based home monitoring, towards improved visual outcomes.

Dietary nutrient intake and cognitive function in the Age-Related Eye Disease Studies 1 and 2.

INTRODUCTION: The objective was to analyze associations between dietary intake of multiple nutrients and altered cognitive function and/or decline. METHODS: Observational analyses of participants (n = 6334) in two randomized trials of nutritional supplements for age-related macular degeneration: Age-Related Eye Disease Study (AREDS) and AREDS2. RESULTS: In AREDS, for 4 of 38 nutrients examined, higher intake quintiles were significantly associated with decreased risk of cognitive impairment on the Modified Mini-Mental State test (<80): ß-carotene, copper, docosahexaenoic acid, and insoluble fiber. In AREDS2, for 13 of 44 nutrients, higher intake quintiles were associated with decreased risk on the Telephone Interview Cognitive Status-Modified (<30). Rate of cognitive decline over up to 10 years was not significantly different with higher intake of any nutrient. DISCUSSION: Higher dietary intake of multiple nutrients, including specific vitamins, minerals, carotenoids, fatty acids, and fiber, was associated with lower risk of cognitive impairment but not slower decline in cognitive function.

Reticular Pseudodrusen: The Third Macular Risk Feature for Progression to Late Age-Related Macular Degeneration: Age-Related Eye Disease Study 2 Report 30.

PURPOSE: To analyze reticular pseudodrusen (RPD) as an independent risk factor for progression to late age-related macular degeneration (AMD), alongside traditional macular risk factors (soft drusen and pigmentary abnormalities) considered simultaneously. DESIGN: Post hoc analysis of 2 clinical trial cohorts: Age-Related Eye Disease Study (AREDS) and AREDS2. PARTICIPANTS: Eyes with no late AMD at baseline in AREDS (6959 eyes, 3780 participants) and AREDS2 (3355 eyes, 2056 participants). METHODS: Color fundus photographs (CFPs) from annual visits were graded for soft drusen, pigmentary abnormalities, and late AMD. Presence of RPD was from grading of fundus autofluorescence images (AREDS2) and deep learning grading of CFPs (AREDS). Proportional hazards regression analyses were performed, considering AREDS AMD severity scales (modified simplified severity scale [person] and 9-step scale [eye]) and RPD presence simultaneously. MAIN OUTCOME MEASURES: Progression to late AMD, geographic atrophy (GA), and neovascular AMD. RESULTS: In AREDS, for late AMD analyses by person, in a model considering the simplified severity scale simultaneously, RPD presence was associated with a higher risk of progression: hazard ratio (HR), 2.15 (95% confidence interval [CI], 1.75-2.64). However, the risk associated with RPD presence differed at different severity scale levels: HR, 3.23 (95% CI, 1.60-6.51), HR, 3.81 (95% CI, 2.38-6.10), HR, 2.28 (95% CI, 1.59-3.27), and HR, 1.64 (95% CI, 1.20-2.24), at levels 0-1, 2, 3, and 4, respectively. Considering the 9-step scale (by eye), RPD presence was associated with higher risk: HR, 2.54 (95% CI, 2.07-3.13). The HRs were 5.11 (95% CI, 3.93-6.66) at levels 1-6 and 1.78 (95% CI, 1.43-2.22) at levels 7 and 8. In AREDS2, by person, RPD presence was not associated with higher risk: HR, 1.18 (95% CI, 0.90-1.56); by eye, it was HR, 1.57 (95% CI, 1.31-1.89). In both cohorts, RPD presence carried a higher risk for GA than neovascular AMD. CONCLUSIONS: Reticular pseudodrusen represent an important risk factor for progression to late AMD, particularly GA. However, the added risk varies markedly by severity level, with highly increased risk at lower/moderate levels and less increased risk at higher levels. Reticular pseudodrusen status should be included in updated AMD classification systems, risk calculators, and clinical trials.

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

OBJECTIVE: Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD. PURPOSE: To delineate the state of AI for AMD, including current data, standards, achievements, and challenges. METHODS: Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus. RESULTS: Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization. CONCLUSIONS: Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.

DeepLensNet: Deep Learning Automated Diagnosis and Quantitative Classification of Cataract Type and Severity.

PURPOSE: To develop deep learning models to perform automated diagnosis and quantitative classification of age-related cataract from anterior segment photographs. DESIGN: DeepLensNet was trained by applying deep learning models to the Age-Related Eye Disease Study (AREDS) dataset. PARTICIPANTS: A total of 18 999 photographs (6333 triplets) from longitudinal follow-up of 1137 eyes (576 AREDS participants). METHODS: Deep learning models were trained to detect and quantify nuclear sclerosis (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical lens opacity (CLO; scale 0%-100%) and posterior subcapsular cataract (PSC; scale 0%-100%) from retroillumination photographs. DeepLensNet performance was compared with that of 14 ophthalmologists and 24 medical students. MAIN OUTCOME MEASURES: Mean squared error (MSE). RESULTS: On the full test set, mean MSE for DeepLensNet was 0.23 (standard deviation [SD], 0.01) for NS, 13.1 (SD, 1.6) for CLO, and 16.6 (SD, 2.4) for PSC. On a subset of the test set (substantially enriched for positive cases of CLO and PSC), for NS, mean MSE for DeepLensNet was 0.23 (SD, 0.02), compared with 0.98 (SD, 0.24; P = 0.000001) for the ophthalmologists and 1.24 (SD, 0.34; P = 0.000005) for the medical students. For CLO, mean MSE was 53.5 (SD, 14.8), compared with 134.9 (SD, 89.9; P = 0.003) for the ophthalmologists and 433.6 (SD, 962.1; P = 0.0007) for the medical students. For PSC, mean MSE was 171.9 (SD, 38.9), compared with 176.8 (SD, 98.0; P = 0.67) for the ophthalmologists and 398.2 (SD, 645.4; P = 0.18) for the medical students. In external validation on the Singapore Malay Eye Study (sampled to reflect the cataract severity distribution in AREDS), the MSE for DeepSeeNet was 1.27 for NS and 25.5 for PSC. CONCLUSIONS: DeepLensNet performed automated and quantitative classification of cataract severity for all 3 types of age-related cataract. For the 2 most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), it was similar. DeepLensNet may have wide potential applications in both clinical and research domains. In the future, such approaches may increase the accessibility of cataract assessment globally. The code and models are available at https://github.com/ncbi/deeplensnet.

Comparison of agents using higher dose anti-VEGF therapy for treatment-resistant neovascular age-related macular degeneration.

PURPOSE: To explore the comparative efficacy and safety of higher dose intravitreal bevacizumab, ranibizumab, or aflibercept for treatment-resistant neovascular age-related macular degeneration (nAMD). METHODS: Retrospective analysis of 37 eyes of 35 patients with treatment-resistant nAMD divided into 3 cohorts based on high-dose treatment received: 3 mg aflibercept, 0.75 mg or 1.0 mg ranibizumab, and 1.8 mg or 2.5 mg bevacizumab. The eyes were analyzed at standardized time points up to 48 months. Included eyes demonstrated active nAMD with persistent exudation on imaging for at least 6 months with at least 4 anti-VEGF injections during this time. Outcomes included change in visual acuity (VA), central retinal thickness (CRT), intraocular pressure (IOP), retinal morphology, adverse event occurrence, and yearly intravitreal injection (IVI) rate. RESULTS: There was no significant difference in VA or IOP change compared to the initiation of high-dose treatment for any agent or comparing between agents at any time point (p > 0.05). CRT improved at month 1, 3, 6, and 12 with all 3 agents (p < 0.05 for all) with a greater CRT reduction seen for ranibizumab than aflibercept at month 6 (p < 0.05), although baseline CRT was greater in the ranibizumab group than the aflibercept group (p < 0.05). Mean absolute CRT was similar at month 6 for all agents (p > 0.05). IVI rates pre- and post-conversion to higher-dose therapy were similar (1 injection per 5.7-6.4 weeks). Mean follow-up was 22.8 months. CONCLUSIONS: Higher dose therapy may achieve improved anatomic outcomes and maintain vision, but frequent injections are required to achieve this. There was no detected difference in efficacy or safety between agents.

MULTIMODAL ASSESSMENTS OF DRUSENOID PIGMENT EPITHELIAL DETACHMENTS IN THE AGE-RELATED EYE DISEASE STUDY 2 ANCILLARY SPECTRAL-DOMAIN OPTICAL COHERENCE TOMOGRAPHY STUDY COHORT.

PURPOSE: To identify features correlating with drusenoid pigment epithelial detachment (DPED) progression in the Age-Related Eye Disease Study 2 Ancillary spectral-domain optical coherence tomography study cohort. METHODS: In this retrospective analysis of a prospective longitudinal study, eyes with intermediate age-related macular degeneration and DPEDs were followed longitudinally with annual multimodal imaging. RESULTS: Thirty-one eyes of 25 participants (mean age 72.6 years) in the Age-Related Eye Disease Study 2 Ancillary spectral-domain OCT substudy (A2A study) had DPED identified in color fundus images. Spectral-domain optical coherence tomography inspection confirmed a subretinal pigment epithelium drusenoid elevation of ≥433 µm diameter in 25 eyes (80.6%). Twenty-four of these eyes were followed longitudinally (median 4.0 years), during which 7 eyes (29.2%) underwent DPED collapse (with 3/7 further progressing to geographic atrophy), 6 (25.0%) developing neovascular age-related macular degeneration, and 11 (45.8%) maintaining DPED persistence without late age-related macular degeneration. On Kaplan-Meier analysis, mean time to DPED collapse was 3.9 years. Both DPED collapse and progression to neovascular age-related macular degeneration were preceded by the presence of hyperreflective foci over the DPED. CONCLUSION: The natural history of DPED comprises collapse (sometimes followed by the development of atrophy), vascularization followed by exudation, or DPED persistence. Spectral-domain optical coherence tomography can confirm retinal pigment epithelial elevation caused by drusenoid accumulation and facilitate the identification of high-risk features that correlate with progression.

Assessing bidirectional associations between cognitive impairment and late age-related macular degeneration in the Age-Related Eye Disease Study 2.

INTRODUCTION: We aimed to investigate bidirectional associations between cognitive impairment and late age-related macular degeneration (AMD). METHODS: Participants in the Age-Related Eye Disease Study 2 (AREDS2) received annual eye examinations and cognitive function testing (e.g., Modified Telephone Interview for Cognitive Status [TICS-M]). We examined bidirectional associations between cognitive impairment (e.g., a TICS-M score < 30) and late AMD at 5 and 10 years. RESULTS: Five thousand one hundred eighty-nine eyes (3157 participants; mean age 72.7 years) were analyzed and followed for a median of 10.4 years. Eyes of participants with cognitive impairment at baseline were more likely to progress to late AMD at 5 years (hazard ratio [HR], 1.24; 95% confidence interval [CI], 1.08-1.43) and 10 years (HR, 1.20; 95% CI, 1.05-1.37) than eyes of participants without cognitive impairment. Worse baseline AMD severity was not associated with developing cognitive impairment. DISCUSSION: Cognitive impairment is associated with late AMD progression in AREDS2. Our finding highlights the importance of eyecare for people with cognitive impairment.

Dietary Nutrient Intake and Progression to Late Age-Related Macular Degeneration in the Age-Related Eye Disease Studies 1 and 2.

PURPOSE: To analyze associations between the dietary intake of multiple nutrients and risk of progression to late age-related macular degeneration (AMD), its subtypes, and large drusen. DESIGN: Post hoc analysis of 2 controlled clinical trial cohorts: Age-Related Eye Disease Study (AREDS) and AREDS2. PARTICIPANTS: Eyes with no late AMD at baseline among AREDS participants (n = 4504) and AREDS2 participants (n = 3738) totaled 14 135 eyes. Mean age was 71.0 years (standard deviation, 6.7 years), and 56.5% of patients were women. METHODS: Fundus photographs were collected at annual study visits and graded centrally for late AMD. Dietary intake of multiple nutrients was calculated from food frequency questionnaires. MAIN OUTCOME MEASURES: Progression to late AMD, geographic atrophy (GA), neovascular AMD, and (separate analyses) large drusen. RESULTS: Over median follow-up of 10.2 years, of the 14 135 eyes, 32.7% progressed to late AMD. For 9 nutrients, intake quintiles 4 or 5 (vs. 1) were associated significantly (P ≤ 0.0005) with decreased risk of late AMD: vitamin A, vitamin B6, vitamin C, folate, ß-carotene, lutein and zeaxanthin, magnesium, copper, and alcohol. For 3 nutrients, quintiles 4 or 5 were associated significantly with increased risk: saturated fatty acid, monounsaturated fatty acid, and oleic acid. Similar results were observed for GA. Regarding neovascular AMD, 9 nutrients were associated nominally with decreased risk-vitamin A, vitamin B6, ß-carotene, lutein and zeaxanthin, magnesium, copper, docosahexaenoic acid, omega-3 fatty acid, and alcohol-and 3 nutrients were associated with increased risk-saturated fatty acid, monounsaturated fatty acid, and oleic acid. In separate analyses (n = 5399 eyes of 3164 AREDS participants), 12 nutrients were associated nominally with decreased risk of large drusen. CONCLUSIONS: Higher dietary intake of multiple nutrients, including minerals, vitamins, and carotenoids, is associated with decreased risk of progression to late AMD. These associations are stronger for GA than for neovascular AMD. The same nutrients also tend to show protective associations against large drusen development. Strong genetic interactions exist for some nutrient-genotype combinations, particularly omega-3 fatty acids and CFH. These data may justify further research into underlying mechanisms and randomized trials of supplementation.

Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.

PURPOSE: To evaluate the performance of retinal specialists in detecting retinal fluid presence in spectral domain OCT (SD-OCT) scans from eyes with age-related macular degeneration (AMD) and compare performance with an artificial intelligence algorithm. DESIGN: Prospective comparison of retinal fluid grades from human retinal specialists and the Notal OCT Analyzer (NOA) on SD-OCT scans from 2 common devices. PARTICIPANTS: A total of 1127 eyes of 651 Age-Related Eye Disease Study 2 10-year Follow-On Study (AREDS2-10Y) participants with SD-OCT scans graded by reading center graders (as the ground truth). METHODS: The AREDS2-10Y investigators graded each SD-OCT scan for the presence/absence of intraretinal and subretinal fluid. Separately, the same scans were graded by the NOA. MAIN OUTCOME MEASURES: Accuracy (primary), sensitivity, specificity, precision, and F1-score. RESULTS: Of the 1127 eyes, retinal fluid was present in 32.8%. For detecting retinal fluid, the investigators had an accuracy of 0.805 (95% confidence interval [CI], 0.780-0.828), a sensitivity of 0.468 (95% CI, 0.416-0.520), a specificity of 0.970 (95% CI, 0.955-0.981). The NOA metrics were 0.851 (95% CI, 0.829-0.871), 0.822 (95% CI, 0.779-0.859), 0.865 (95% CI, 0.839-0.889), respectively. For detecting intraretinal fluid, the investigator metrics were 0.815 (95% CI, 0.792-0.837), 0.403 (95% CI, 0.349-0.459), and 0.978 (95% CI, 0.966-0.987); the NOA metrics were 0.877 (95% CI, 0.857-0.896), 0.763 (95% CI, 0.713-0.808), and 0.922 (95% CI, 0.902-0.940), respectively. For detecting subretinal fluid, the investigator metrics were 0.946 (95% CI, 0.931-0.958), 0.583 (95% CI, 0.471-0.690), and 0.973 (95% CI, 0.962-0.982); the NOA metrics were 0.863 (95% CI, 0.842-0.882), 0.940 (95% CI, 0.867-0.980), and 0.857 (95% CI, 0.835-0.877), respectively. CONCLUSIONS: In this large and challenging sample of SD-OCT scans obtained with 2 common devices, retinal specialists had imperfect accuracy and low sensitivity in detecting retinal fluid. This was particularly true for intraretinal fluid and difficult cases (with lower fluid volumes appearing on fewer B-scans). Artificial intelligence-based detection achieved a higher level of accuracy. This software tool could assist physicians in detecting retinal fluid, which is important for diagnostic, re-treatment, and prognostic tasks.

Quantitative Analysis of OCT for Neovascular Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: To apply a deep learning algorithm for automated, objective, and comprehensive quantification of OCT scans to a large real-world dataset of eyes with neovascular age-related macular degeneration (AMD) and make the raw segmentation output data openly available for further research. DESIGN: Retrospective analysis of OCT images from the Moorfields Eye Hospital AMD Database. PARTICIPANTS: A total of 2473 first-treated eyes and 493 second-treated eyes that commenced therapy for neovascular AMD between June 2012 and June 2017. METHODS: A deep learning algorithm was used to segment all baseline OCT scans. Volumes were calculated for segmented features such as neurosensory retina (NSR), drusen, intraretinal fluid (IRF), subretinal fluid (SRF), subretinal hyperreflective material (SHRM), retinal pigment epithelium (RPE), hyperreflective foci (HRF), fibrovascular pigment epithelium detachment (fvPED), and serous PED (sPED). Analyses included comparisons between first- and second-treated eyes by visual acuity (VA) and race/ethnicity and correlations between volumes. MAIN OUTCOME MEASURES: Volumes of segmented features (mm3) and central subfield thickness (CST) (µm). RESULTS: In first-treated eyes, the majority had both IRF and SRF (54.7%). First-treated eyes had greater volumes for all segmented tissues, with the exception of drusen, which was greater in second-treated eyes. In first-treated eyes, older age was associated with lower volumes for RPE, SRF, NSR, and sPED; in second-treated eyes, older age was associated with lower volumes of NSR, RPE, sPED, fvPED, and SRF. Eyes from Black individuals had higher SRF, RPE, and serous PED volumes compared with other ethnic groups. Greater volumes of the majority of features were associated with worse VA. CONCLUSIONS: We report the results of large-scale automated quantification of a novel range of baseline features in neovascular AMD. Major differences between first- and second-treated eyes, with increasing age, and between ethnicities are highlighted. In the coming years, enhanced, automated OCT segmentation may assist personalization of real-world care and the detection of novel structure-function correlations. These data will be made publicly available for replication and future investigation by the AMD research community.

MASSIVE ADVANCING NONEXUDATIVE TYPE 1 CHOROIDAL NEOVASCULARIZATION IN CTRP5 LATE-ONSET RETINAL DEGENERATION: Longitudinal Findings on Multimodal Imaging and Implications for Age-Related Macular Degeneration.

PURPOSE: To describe longitudinal multimodal imaging findings of nonexudative choroidal neovascularization in CTRP5 late-onset retinal degeneration. METHODS: Four patients with CTRP5-positive late-onset retinal degeneration underwent repeated ophthalmoscopic examination and multimodal imaging. All four patients (two siblings and their cousins, from a pedigree described previously) had the heterozygous S163R mutation. RESULTS: All four patients demonstrated large subretinal lesions in the mid-peripheral retina of both eyes. The lesions were characterized by confluent hypercyanescence with hypocyanescent borders on indocyanine green angiography, faintly visible branching vascular networks with absent/minimal leakage on fluorescein angiography, Type 1 neovascularization on optical coherence tomography angiography, and absent retinal fluid, consistent with nonexudative choroidal neovascularization. The neovascular membranes enlarged substantially over time and the birth of new membranes was observed, but all lesions remained nonexudative/minimally exudative. Without treatment, all involved retinal areas remained free of atrophy and subretinal fibrosis. CONCLUSION: We report the existence of massive advancing nonexudative Type 1 choroidal neovascularization in CTRP5 late-onset retinal degeneration. These findings have implications for age-related macular degeneration. They provide a monogenic model system for studying the mechanisms underlying the distinct events of choroidal neovascularization development, enlargement, progression to exudation, and atrophy in age-related macular degeneration. They suggest that choroidal hypoperfusion precedes neovascularization and that nonexudative neovascularization may protect against atrophy.

Age-Related Macular Degeneration: Epidemiology and Clinical Aspects.

Age-related macular degeneration (AMD) is a degenerative disease of the human retina affecting individuals over the age of 55 years. This heterogeneous condition arises from a complex interplay between age, genetics, and environmental factors including smoking and diet. It is the leading cause of blindness in industrialized countries. Worldwide, the number of people with AMD is predicted to increase from 196 million in 2020 to 288 million by 2040. By this time, Asia is predicted to have the largest number of people with the disease. Distinct patterns of AMD prevalence and phenotype are seen between geographical areas that are not explained fully by disparities in population structures. AMD is classified into early, intermediate, and late stages. The early and intermediate stages, when visual symptoms are typically absent or mild, are characterized by macular deposits (drusen) and pigmentary abnormalities. Through risk prediction calculators, grading these features helps predict the risk of progression to late AMD. Late AMD is divided into neovascular and atrophic forms, though these can coexist. The defining lesions are macular neovascularization and geographic atrophy, respectively. At this stage, visual symptoms are often severe and irreversible, and can comprise profoundly decreased central vision in both eyes. For these reasons, the condition has major implications for individuals and society, as affected individuals may experience substantially decreased quality of life and independence. Recent advances in retinal imaging have led to the recognition of an expanded set of AMD phenotypes, including reticular pseudodrusen, nonexudative macular neovascularization, and subtypes of atrophy. These developments may lead to refinements in current classification systems.

Intravitreous treatment of severe ocular von Hippel-Lindau disease using a combination of the VEGF inhibitor, ranibizumab and PDGF inhibitor, E10030: Results from a phase 1/2 clinical trial.

BACKGROUND: Treatment options for severe ocular von Hippel-Lindau (VHL) disease are limited. This trial evaluated preliminary safety and potential efficacy of combination intravitreous injection with ranibizumab, a vascular endothelial growth factor (VEGF) inhibitor, and E10030, a PDGF inhibitor, for eyes with VHL disease-associated retinal hemangioblastoma (RH) not amenable or responsive to thermal laser photocoagulation. METHODS: This was a prospective, single-arm, open-label phase 1/2 study, comprised of three adults with VHL-associated RH and vision loss. Intravitreous injections of ranibizumab (0.5 mg) and E10030 (1.5 mg) were given unilaterally every 4 weeks in the study eye through 16 weeks, then every 8 weeks through 48 weeks. Supplementary standard care therapies were allowed without restriction after 40 weeks. The primary outcome was the ocular and systemic adverse effect profile at 52 weeks. Secondary outcomes included changes in best-corrected visual acuity (BCVA), RH size, exudation, epiretinal proliferation and retinal traction, and need for ablative treatment of RH or ocular surgery at week 52. RESULTS: Three participants each received nine injections prior to week 52 and were followed for 104 weeks. One participant manifested mild episodic ocular hypertension in the study eye. Change in BCVA in the study eye at week 52 for the three participants was -5, -12 and +2 letters. No reduction in RH size was measured at 52 weeks. Variable mild improvements in exudation in two participants at week 16 were not sustained through week 52. CONCLUSIONS: Combination intravitreous injection with ranibizumab and E10030 demonstrated a reasonable preliminary safety profile, but limited treatment effect.

Deep Learning Automated Detection of Reticular Pseudodrusen from Fundus Autofluorescence Images or Color Fundus Photographs in AREDS2.

PURPOSE: To develop deep learning models for detecting reticular pseudodrusen (RPD) using fundus autofluorescence (FAF) images or, alternatively, color fundus photographs (CFP) in the context of age-related macular degeneration (AMD). DESIGN: Application of deep learning models to the Age-Related Eye Disease Study 2 (AREDS2) dataset. PARTICIPANTS: FAF and CFP images (n = 11 535) from 2450 AREDS2 participants. Gold standard labels from reading center grading of the FAF images were transferred to the corresponding CFP images. METHODS: A deep learning model was trained to detect RPD in eyes with intermediate to late AMD using FAF images (FAF model). Using label transfer from FAF to CFP images, a deep learning model was trained to detect RPD from CFP (CFP model). Performance was compared with 4 ophthalmologists using a random subset from the full test set. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC), κ value, accuracy, and F1 score. RESULTS: The FAF model had an AUC of 0.939 (95% confidence interval [CI], 0.927-0.950), a κ value of 0.718 (95% CI, 0.685-0.751), and accuracy of 0.899 (95% CI, 0.887-0.911). The CFP model showed equivalent values of 0.832 (95% CI, 0.812-0.851), 0.470 (95% CI, 0.426-0.511), and 0.809 (95% CI, 0.793-0.825), respectively. The FAF model demonstrated superior performance to 4 ophthalmologists, showing a higher κ value of 0.789 (95% CI, 0.675-0.875) versus a range of 0.367 to 0.756 and higher accuracy of 0.937 (95% CI, 0.907-0.963) versus a range of 0.696 to 0.933. The CFP model demonstrated substantially superior performance to 4 ophthalmologists, showing a higher κ value of 0.471 (95% CI, 0.330-0.606) versus a range of 0.105 to 0.180 and higher accuracy of 0.844 (95% CI, 0.798-0.886) versus a range of 0.717 to 0.814. CONCLUSIONS: Deep learning-enabled automated detection of RPD presence from FAF images achieved a high level of accuracy, equal or superior to that of ophthalmologists. Automated RPD detection using CFP achieved a lower accuracy that still surpassed that of ophthalmologists. Deep learning models can assist, and even augment, the detection of this clinically important AMD-associated lesion.