Publications by authors named "Emily Y Chew"

317 Publications

From data to deployment: the Collaborative Communities on Ophthalmic Imaging roadmap for artificial intelligence in age-related macular degeneration.

Ophthalmology 2022 Jan 8. Epub 2022 Jan 8.

Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois, USA. Electronic address:

Importance: Healthcare 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 FDA-approved AI devices for AMD.

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

Findings: Existing infrastructure for robust AI development for AMD includes several large, labeled datasets of color fundus photography (CFP) and optical coherence tomography (OCT) images. However, image data often does not contain meta-data 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 (ML) 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 real-world generalization.

Conclusions: AND RELEVANCE: 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 curation 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 will eventually benefit providers, healthcare systems, and patients.
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http://dx.doi.org/10.1016/j.ophtha.2022.01.002DOI Listing
January 2022

Comparison of ETDRS 7-Field to 4-Widefield Digital Imaging in the Evaluation of Diabetic Retinopathy Severity.

Transl Vis Sci Technol 2022 01;11(1):13

Biostatistics Center and Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Rockville, MD, USA.

Purpose: To compare Early Treatment Diabetic Retinopathy Study (ETDRS) severity levels between two digital fundus imaging protocols for research studies of diabetic retinopathy: the gold standard 7-field (7F) imaging and the more recent 4-widefield (4W) imaging.

Methods: Two hundred twenty-two participants enrolled in the Diabetes Prevention Program Outcomes Study underwent concurrent 7F and 4W imaging. The ETDRS levels from 220 paired gradable images were determined by masked graders. Each image was graded by two independent graders with adjudication by a senior grader, if necessary. Percent agreement between graders and between imaging protocols was evaluated with kappa statistics and weighted kappa statistics.

Results: Of 220 gradable eyes, diabetic retinopathy was seen in 11.8%; this was mild in 10.4% and more than mild in 1.4% using 7F imaging. The ETDRS levels showed exact agreement of 95% between 7F and 4W imaging (weighted kappa 0.86). Intergrader agreement for each modality had exact agreement of 89% (weighted kappa of 0.73) for 7F and 91% (weighted kappa 0.77) for 4W.

Conclusions: There is substantial agreement in the ETDRS severity level between the 7F and 4W digital imaging protocols, demonstrating that the two imaging protocols are interchangeable. Both 4W and 7F digital imaging protocols can be used for assessing ETDRS levels, even in populations with minimal diabetic retinopathy.

Translational Relevance: The 4W protocol requires fewer images than the 7F, is more comfortable for the patients, is easier for photographic capture, and provides diabetic retinopathy data that is equivalent to the 7F imaging protocol.
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http://dx.doi.org/10.1167/tvst.11.1.13DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762689PMC
January 2022

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

Ophthalmology 2022 Jan 3. Epub 2022 Jan 3.

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA. Electronic address:

Purpose: To develop and evaluate deep learning models to perform automated diagnosis and quantitative classification of age-related cataract, including all three anatomical types, from anterior segment photographs.

Design: Application of deep learning models to Age-Related Eye Disease Study (AREDS) dataset.

Participants: 18,999 photographs (6,333 triplets) from longitudinal follow-up of 1,137 eyes (576 AREDS participants).

Methods: Deep learning models were trained to detect and quantify nuclear cataract (NS; scale 0.9-7.1) from 45-degree slit-lamp photographs and cortical (CLO; scale 0-100%) and posterior subcapsular (PSC; scale 0-100%) cataract from retroillumination photographs. Model performance was compared with that of 14 ophthalmologists and 24 medical students. The ground truth labels were from reading center grading.

Main Outcome Measures: Mean squared error (MSE).

Results: On the full test set, mean MSE values for the deep learning models were: 0.23 (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 the models was 0.23 (SD 0.02), compared to 0.98 (SD 0.23; p=0.000001) for the ophthalmologists, and 1.24 (SD 0.33; p=0.000005) for the medical students. For CLO, mean MSE values were 53.5 (SD 14.8), compared to 134.9 (SD 89.9; p=0.003) and 422.0 (SD 944.4; p=0.0007), respectively. For PSC, mean MSE values were 171.9 (SD 38.9), compared to 176.8 (SD 98.0; p=0.67) and 395.2 (SD 632.5; p=0.18), respectively. In external validation on the Singapore Malay Eye Study (sampled to reflect the distribution of cataract severity in AREDS), MSE was 1.27 for NS and 25.5 for PSC.

Conclusions: A deep learning framework was able to perform automated and quantitative classification of cataract severity for all three types of age-related cataract. For the two most common types (NS and CLO), the accuracy was significantly superior to that of ophthalmologists; for the least common type (PSC), the accuracy was similar. The framework 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 publicly available at https://XXX.
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http://dx.doi.org/10.1016/j.ophtha.2021.12.017DOI Listing
January 2022

Conversion of Central Subfield Thickness Measurements of Diabetic Macular Edema Across Cirrus and Spectralis Optical Coherence Tomography Instruments.

Transl Vis Sci Technol 2021 12;10(14):34

Ophthalmic Research Consultants, LLC, Waxhaw, North Carolina, USA.

Purpose: Develop equations to convert Cirrus central subfield thickness (CST) to Spectralis CST equivalents and vice versa in eyes with diabetic macular edema (DME).

Methods: The DRCR Retina Network Protocol O data were split randomly to train (70% sample) and validate (30% sample) conversion equations. Data from an independent study (CADME) also validated the equations. Bland-Altman 95% limits of agreement between predicted and observed values evaluated the equations.

Results: Protocol O included 374 CST scan pairs from 187 eyes (107 participants). The CADME study included 150 scan pairs of 37 eyes (37 participants). Proposed conversion equations are Spectralis = 40.78 + 0.95 × Cirrus and Cirrus = 1.82 + 0.94 × Spectralis regardless of age, sex, or CST. Predicted values were within 10% of observed values in 101 (90%) of Spectralis and 99 (88%) of Cirrus scans in the validation data; and in 136 (91%) of the Spectralis and 148 (99%) of the Cirrus scans in the CADME data. Adjusting for within-eye correlations, 95% of conversions are estimated to be within 17% (95% confidence interval, 14%-21%) of CST on Spectralis and within 22% (95% confidence interval, 18%-28%) of CST on Cirrus.

Conclusions: Conversion equations developed in this study allow the harmonization of CST measurements for eyes with DME using a mix of current Cirrus and Spectralis device images.

Translational Relevance: The CSTs measured on Cirrus and Spectralis devices are not directly comparable owing to outer boundary segmentation differences. Converting CST values across spectral domain optical coherence tomography instruments should benefit both clinical research and standard care efforts.
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http://dx.doi.org/10.1167/tvst.10.14.34DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727314PMC
December 2021

Cataract Surgery and the Risk of Developing Late Age-Related Macular Degeneration: The Age-Related Eye Disease Study 2 Report Number 27.

Ophthalmology 2021 Nov 16. Epub 2021 Nov 16.

Clinical Trials Branch, Division of Epidemiology and Clinical Applications, National Eye Institute/National Institutes of Health, Bethesda, Maryland. Electronic address:

Purpose: To evaluate the risk of developing late age-related macular degeneration (AMD) after incident cataract surgery.

Design: A prospective cohort study within a randomized controlled clinical trial of oral supplementation for the treatment of AMD, the Age-Related Eye Disease Study 2 (AREDS2).

Participants: AREDS2 participants aged 50 to 85 years with bilateral large drusen or unilateral late AMD.

Methods: In eyes free of cataract surgery and late AMD at baseline, 2 groups were compared for incident late AMD: (1) eyes that received cataract surgery after the baseline visit and before any evidence of late AMD and (2) eyes that remained phakic until study completion. Eyes with at least 2 years of follow-up after cataract surgery were included in the analysis. We used Cox regression models, matched-pairs analysis, and logistic regression models that were adjusted for age, sex, smoking, education, study treatment group, and AMD severity.

Main Outcome Measures: Late AMD was defined as the presence of geographic atrophy or neovascular AMD detected on annual stereoscopic fundus photographs or as documented by medical records, including intravitreous injections of anti-vascular endothelial growth factor medication.

Results: A total of 1767 eligible eyes (1195 participants) received cataract surgery; 1981 eyes (1524 participants) developed late AMD during a mean (range) follow-up of 9 (1-12) years. The Cox regression model showed no increased risk of developing late AMD after cataract surgery: hazard ratio, 0.96; 95% confidence interval (CI), 0.81-1.13 (P = 0.60) for right eyes and hazard ratio, 1.05; 95% CI, 0.89-1.25 (P = 0.56) for left eyes. Of the matched pairs, late AMD was identified in 408 eyes that received cataract surgery and in 429 phakic controls: odds ratio (OR) 0.92 (95% CI, 0.77-1.10; P = 0.34). The risk of late AMD after cataract surgery from the logistic regression model was not statistically significant (risk ratio, 0.92; 95% CI, 0.56-1.49; P = 0.73).

Conclusions: Cataract surgery did not increase the risk of developing late AMD among AREDS2 participants with up to 10 years of follow-up. This study provides data for counseling AMD patients who might benefit from cataract surgery.
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http://dx.doi.org/10.1016/j.ophtha.2021.11.014DOI Listing
November 2021

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

Alzheimers Dement 2021 Nov 10. Epub 2021 Nov 10.

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.

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.
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http://dx.doi.org/10.1002/alz.12473DOI Listing
November 2021

Revisiting the Question of Genetic Testing for Persons with Age-Related Macular Degeneration.

Authors:
Emily Y Chew

Ophthalmology 2021 11;128(11):1618-1619

Bethesda, Maryland. Electronic address:

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http://dx.doi.org/10.1016/j.ophtha.2021.07.002DOI Listing
November 2021

Visual acuity outcomes after cataract surgery in type 2 diabetes: the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study.

Br J Ophthalmol 2021 Jun 18. Epub 2021 Jun 18.

Division of Epidemiology and Clinical Applications, National Eye Institute, Bethesda, Maryland, USA

Aims: To evaluate visual acuity (VA) outcomes of cataract surgery, and factors associated with good visual outcomes, among a population with diabetes.

Methods: Participants with type 2 diabetes enrolled in The Action to Control Cardiovascular Risk in Diabetes (ACCORD) study and ACCORD-eye substudy. 1136 eyes of 784 ACCORD participants receiving cataract surgery during follow-up (2001-2014) were included. Of these, 362 eyes had fundus photographs gradable for diabetic retinopathy. The main outcome measure was the achievement of postoperative VA of 20/40 or better.

Results: In the sample of 1136 eyes, 762 eyes (67.1%) achieved good visual outcome of 20/40 or better. Factors predictive of good visual outcome were higher level of educational attainment (college vs some high school, OR 2.35 (95% CI 1.44 to 3.82)), bilateral cataract surgery (OR 1.55 (1.14 to 2.10)) and preoperative VA (20/20 or better vs worse than 20/200, OR 10.59 (4.07 to 27.54)). Factors not significantly associated (p>0.05) included age, sex, race, smoking, diabetes duration, blood pressure, lipid levels and haemoglobin A1C (HbA1C). In the subsample of 362 eyes, absence of diabetic retinopathy was associated with good visual outcome (OR 1.73 (1.02 to 2.94)).

Conclusion: Among individuals with diabetes, two-thirds of eyes achieved good visual outcome after cataract surgery. Notable factors associated with visual outcome included preoperative VA and diabetic retinopathy, but not HbA1C, underscoring that while certain ocular measures may help evaluate visual potential, systemic parameters may not be as valuable. Sociodemographic factors might also be important considerations. Although the current visual prognosis after cataract surgery is usually favourable, certain factors still limit the visual potential in those with diabetes.
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http://dx.doi.org/10.1136/bjophthalmol-2020-317793DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8683570PMC
June 2021

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.

Clin Exp Ophthalmol 2021 12 26;49(9):1048-1059. Epub 2021 Oct 26.

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.

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.
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http://dx.doi.org/10.1111/ceo.14001DOI Listing
December 2021

Identification and inference for subgroups with differential treatment efficacy from randomized controlled trials with survival outcomes through multiple testing.

Stat Med 2021 Dec 20;40(29):6523-6540. Epub 2021 Sep 20.

Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

With the uptake of targeted therapies, instead of the "one-fits-all" approach, modern randomized controlled trials (RCTs) often aim to develop treatments that target a subgroup of patients. Motivated by analyzing the Age-Related Eye Disease Study (AREDS) data, a large RCT to study the efficacy of nutritional supplements in delaying the progression of an eye disease, age-related macular degeneration (AMD), we develop a simultaneous inference procedure to identify and infer subgroups with differential treatment efficacy in RCTs with time-to-event outcomes. Specifically, we formulate the multiple testing problem through contrasts and construct their simultaneous confidence intervals, which appropriately control both within- and across-marker multiplicity. Realistic simulations are conducted using real genotype data to evaluate the method performance under various scenarios. The method is then applied to AREDS to assess the efficacy of antioxidants and zinc combination in delaying AMD progression. Multiple gene regions including ESRRB-VASH1 on chromosome 14 have been identified with subgroups showing differential efficacy. We further validate our findings in an independent subsequent RCT, AREDS2, by discovering consistent differential treatment responses in the targeted and non-targeted subgroups identified from AREDS. This multiple-testing-based simultaneous inference approach provides a step forward to confidently identify and infer subgroups in modern drug development.
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http://dx.doi.org/10.1002/sim.9196DOI Listing
December 2021

Imaging endpoints for clinical trials in MacTel type 2.

Eye (Lond) 2021 Aug 13. Epub 2021 Aug 13.

National Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Introduction: Macular Telangiectasia type 2 (MacTel) is a bilateral neurodegenerative disease associated with dysfunction in the serine and lipid metabolism resulting in loss of Muller cells and photoreceptors. Typical structural changes include vascular abnormalities, loss of retinal transparency, redistribution of macular pigment and thinning of the central retina with photoreceptor loss. The presence and extent of photoreceptor loss, as visible on Optical Coherence Tomography (OCT) ("disease severity scale"), correlate with functional loss and the limitation of photoreceptor loss appears to be the most promising therapeutic approach. Ongoing clinical trials of ciliary neurotrophic factor (CNTF) implants for the treatment of MacTel are using this outcome to evaluate efficacy. An ideal outcome measure provides the ability to quantify the extent of the disease progression with precision and reproducibility.

Methods: This review describes the changes and findings on different imaging techniques including fluorescein- and OCT angiography, blue light reflectance, 1- and 2-wavelength autofluorescence and OCT.

Results: The possibilities of objective quantification of the severity of MacTel and correlation with functional characteristics such as best-corrected visual acuity (BCVA) and microperimetry and their applications as quantitative imaging endpoints for clinical treatment trials are discussed. OCT and especially en face OCT could be demonstrated as precise and reproducible methods to quantify the area of photoreceptor loss, which correlated highly significantly with functional loss in microperimetry.

Conclusion: The analysis of the area of photoreceptor loss on en face OCT is the most reliable imaging endpoint for treatment trials in MacTel. This method is already being used in ongoing randomized trials.
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http://dx.doi.org/10.1038/s41433-021-01723-7DOI Listing
August 2021

Cluster Analysis and Genotype-Phenotype Assessment of Geographic Atrophy in Age-Related Macular Degeneration: Age-Related Eye Disease Study 2 Report 25.

Ophthalmol Retina 2021 11 26;5(11):1061-1073. Epub 2021 Jul 26.

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.

Purpose: To explore whether phenotypes in geographic atrophy (GA) secondary to age-related macular degeneration can be separated into 2 or more partially distinct subtypes and if these have different genetic associations. This is important because distinct GA subtypes associated with different genetic factors might require customized therapeutic approaches.

Design: Cluster analysis of participants within a controlled clinical trial, followed by assessment of phenotype-genotype associations.

Participants: Age-Related Eye Disease Study 2 participants with incident GA during study follow-up: 598 eyes of 598 participants.

Methods: Phenotypic features from reading center grading of fundus photographs were subjected to cluster analysis, by k-means and hierarchical methods, in cross-sectional analyses (using 15 phenotypic features) and longitudinal analyses (using 14 phenotypic features). The identified clusters were compared by 4 pathway-based genetic risk scores (complement, extracellular matrix, lipid, and ARMS2). The analyses were repeated in reverse (clustering by genotype and comparison by phenotype).

Main Outcome Measures: Characteristics and quality of cluster solutions, assessed by Calinski-Harabasz scores, unexplained variance, and consistency; and genotype-phenotype associations, assessed by t test.

Results: In cross-sectional phenotypic analyses, k-means identified 2 clusters (labeled A and B), whereas hierarchical clustering identified 4 clusters (C-F); cluster membership differed principally by GA configuration but in few other ways. In longitudinal phenotypic analyses, k-means identified 2 clusters (G and H) that differed principally by smoking status but in few other ways. These 3 sets of cluster divisions were not similar to each other (r ≤ 0.20). Despite adequate power, pairwise cluster comparison by the 4 genetic risk scores demonstrated no significant differences (P > 0.05 for all). In clustering by genotype, k-means identified 2 clusters (I and J). These differed principally at ARMS2, but no significant genotype-phenotype associations were observed (P > 0.05 for all).

Conclusions: Phenotypic clustering resulted in GA subtypes defined principally by GA configuration in cross-sectional analyses, but these were not replicated in longitudinal analyses. These negative findings, together with the absence of significant phenotype-genotype associations, indicate that GA phenotypes may vary continuously across a spectrum, rather than consisting of distinct subtypes that arise from separate genetic causes.
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http://dx.doi.org/10.1016/j.oret.2021.07.006DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8578299PMC
November 2021

Gene Set Enrichment Analsyes Identify Pathways Involved in Genetic Risk for Diabetic Retinopathy.

Am J Ophthalmol 2022 Jan 21;233:111-123. Epub 2021 Jun 21.

From the Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary; Broad Institute of Harvard and MIT, Cambridge, Massachusetts.

To identify functionally related genes associated with diabetic retinopathy (DR) risk using gene set enrichment analyses applied to genome-wide association study meta-analyses.

Methods: We analyzed DR GWAS meta-analyses performed on 3246 Europeans and 2611 African Americans with type 2 diabetes. Gene sets relevant to 5 key DR pathophysiology processes were investigated: tissue injury, vascular events, metabolic events and glial dysregulation, neuronal dysfunction, and inflammation. Keywords relevant to these processes were queried in 4 pathway and ontology databases. Two GSEA methods, Meta-Analysis Gene set Enrichment of variaNT Associations (MAGENTA) and Multi-marker Analysis of GenoMic Annotation (MAGMA), were used. Gene sets were defined to be enriched for gene associations with DR if the P value corrected for multiple testing (Pcorr) was <.05.

Results: Five gene sets were significantly enriched for numerous modest genetic associations with DR in one method (MAGENTA or MAGMA) and also at least nominally significant (uncorrected P < .05) in the other method. These pathways were regulation of the lipid catabolic process (2-fold enrichment, Pcorr = .014); nitric oxide biosynthesis (1.92-fold enrichment, Pcorr = .022); lipid digestion, mobilization, and transport (1.6-fold enrichment, P = .032); apoptosis (1.53-fold enrichment, P = .041); and retinal ganglion cell degeneration (2-fold enrichment, Pcorr = .049). The interferon gamma (IFNG) gene, previously implicated in DR by protein-protein interactions in our GWAS, was among the top ranked genes in the nitric oxide pathway (best variant P = .0001).

Conclusions: These GSEA indicate that variants in genes involved in oxidative stress, lipid transport and catabolism, and cell degeneration are enriched for genes associated with DR risk. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.
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http://dx.doi.org/10.1016/j.ajo.2021.06.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8678352PMC
January 2022

Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk.

Transl Vis Sci Technol 2021 02;10(2):29

Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.

Purpose: Because age-related macular degeneration (AMD) is a progressive disorder and advanced AMD is currently hard to cure, an accurate and informative prediction of a person's AMD risk using genetic information is desirable for early diagnosis and potential individualized clinical management. The objective of this study was to develop and validate novel prediction models for AMD risk using large genome-wide association studies datasets with different machine learning approaches.

Methods: Genotype data from 32,215 Caucasian individuals with age of ≥50 years from the International AMD Genomics Consortium in dbGaP were used to establish and test prediction models for AMD risk. Four different machine learning approaches-neural network, lasso regression, support vector machine, and random forest-were implemented. A standard logistic regression model using a genetic risk score was also considered.

Results: All machine learning-based methods achieved satisfactory performance for predicting advanced AMD cases (vs. normal controls) (area under the curve = 0.81-0.82, Brier score = 0.17-0.18 in a separate test dataset) and any stage AMD (vs. normal controls) (area under the curve = 0.78-0.79, Brier score = 0.18-0.20 in a separate test dataset). The prediction performance was further validated in an independent dataset of 783 subjects from UK Biobank (area under the curve = 0.67).

Conclusions: By applying multiple state-of-art machine learning approaches on large AMD genome-wide association studies datasets, the predictive models we established can provide an accurate estimation of an individual's AMD risk profile based on genetic information along with age. The online prediction interface is available at: https://yanq.shinyapps.io/no_vs_amd_NN/.

Translational Relevance: The accurate and individualized risk prediction model interface will greatly improve early diagnosis and enhance tailored clinical management of AMD.
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http://dx.doi.org/10.1167/tvst.10.2.29DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900884PMC
February 2021

Reply.

Ophthalmology 2021 08 10;128(8):e41. Epub 2021 May 10.

National Eye Institute| National Institutes of Health, Bethesda, Maryland.

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http://dx.doi.org/10.1016/j.ophtha.2021.04.011DOI Listing
August 2021

Updated Numbers on the State of Visual Acuity Loss and Blindness in the US.

Authors:
Emily Y Chew

JAMA Ophthalmol 2021 Jul;139(7):723-724

Division of Epidemiology and Clinical Applications, National Eye Institute/National Institutes of Health, Bethesda, Maryland.

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http://dx.doi.org/10.1001/jamaophthalmol.2021.0521DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8330344PMC
July 2021

Age-related macular degeneration.

Nat Rev Dis Primers 2021 05 6;7(1):31. Epub 2021 May 6.

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.

Age-related macular degeneration (AMD) is the leading cause of legal blindness in the industrialized world. AMD is characterized by accumulation of extracellular deposits, namely drusen, along with progressive degeneration of photoreceptors and adjacent tissues. AMD is a multifactorial disease encompassing a complex interplay between ageing, environmental risk factors and genetic susceptibility. Chronic inflammation, lipid deposition, oxidative stress and impaired extracellular matrix maintenance are strongly implicated in AMD pathogenesis. However, the exact interactions of pathophysiological events that culminate in drusen formation and the associated degeneration processes remain to be elucidated. Despite tremendous advances in clinical care and in unravelling pathophysiological mechanisms, the unmet medical need related to AMD remains substantial. Although there have been major breakthroughs in the treatment of exudative AMD, no efficacious treatment is yet available to prevent progressive irreversible photoreceptor degeneration, which leads to central vision loss. Compelling progress in high-resolution retinal imaging has enabled refined phenotyping of AMD in vivo. These insights, in combination with clinicopathological and genetic correlations, have underscored the heterogeneity of AMD. Hence, our current understanding promotes the view that AMD represents a disease spectrum comprising distinct phenotypes with different mechanisms of pathogenesis. Hence, tailoring therapeutics to specific phenotypes and stages may, in the future, be the key to preventing irreversible vision loss.
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http://dx.doi.org/10.1038/s41572-021-00265-2DOI Listing
May 2021

Age-Related Macular Degeneration: Epidemiology and Clinical Aspects.

Adv Exp Med Biol 2021 ;1256:1-31

Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.

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.
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http://dx.doi.org/10.1007/978-3-030-66014-7_1DOI Listing
April 2021

Multimodal, multitask, multiattention (M3) deep learning detection of reticular pseudodrusen: Toward automated and accessible classification of age-related macular degeneration.

J Am Med Inform Assoc 2021 06;28(6):1135-1148

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA.

Objective: Reticular pseudodrusen (RPD), a key feature of age-related macular degeneration (AMD), are poorly detected by human experts on standard color fundus photography (CFP) and typically require advanced imaging modalities such as fundus autofluorescence (FAF). The objective was to develop and evaluate the performance of a novel multimodal, multitask, multiattention (M3) deep learning framework on RPD detection.

Materials And Methods: A deep learning framework (M3) was developed to detect RPD presence accurately using CFP alone, FAF alone, or both, employing >8000 CFP-FAF image pairs obtained prospectively (Age-Related Eye Disease Study 2). The M3 framework includes multimodal (detection from single or multiple image modalities), multitask (training different tasks simultaneously to improve generalizability), and multiattention (improving ensembled feature representation) operation. Performance on RPD detection was compared with state-of-the-art deep learning models and 13 ophthalmologists; performance on detection of 2 other AMD features (geographic atrophy and pigmentary abnormalities) was also evaluated.

Results: For RPD detection, M3 achieved an area under the receiver-operating characteristic curve (AUROC) of 0.832, 0.931, and 0.933 for CFP alone, FAF alone, and both, respectively. M3 performance on CFP was very substantially superior to human retinal specialists (median F1 score = 0.644 vs 0.350). External validation (the Rotterdam Study) demonstrated high accuracy on CFP alone (AUROC, 0.965). The M3 framework also accurately detected geographic atrophy and pigmentary abnormalities (AUROC, 0.909 and 0.912, respectively), demonstrating its generalizability.

Conclusions: This study demonstrates the successful development, robust evaluation, and external validation of a novel deep learning framework that enables accessible, accurate, and automated AMD diagnosis and prognosis.
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http://dx.doi.org/10.1093/jamia/ocaa302DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8200273PMC
June 2021

Confident identification of subgroups from SNP testing in RCTs with binary outcomes.

Biom J 2021 Mar 9. Epub 2021 Mar 9.

Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.

In modern drug development, genotype information becomes more frequently collected in randomized controlled trials (RCTs) for individualized risk prediction and personalized medicine development. Finding single nucleotide polymorphisms (SNPs) that are predictive of differential treatment efficacy, measured by a clinical outcome, is fundamentally different and more challenging than the traditional association test for a quantitative trait. With the objective to confidently identify and infer genetic subgroups with enhanced treatment efficacy from a large RCT for an eye disease, age-related macular degeneration (AMD), where the clinical endpoint is binary (progressed or not), we propose a novel SNP-testing procedure for binary clinical outcomes. Specifically, we formulate four contrasts to simultaneously assess all possible genetic effects on a logic-respecting efficacy measure, the relative risk (between treatment and control). Our method controls both within- and across-SNP multiplicity rigorously. We then use real genotype data to perform chromosome-wide simulations to evaluate our method performance and to provide practical recommendations. Finally, we apply the proposed method to perform a genome-wide SNP testing for the AMD trial and successfully identify multiple gene regions with genetic subgroups exhibiting enhanced efficacy in terms of decreasing the AMD progression rate.
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http://dx.doi.org/10.1002/bimj.202000170DOI Listing
March 2021

Gene-based analysis of bi-variate survival traits via functional regressions with applications to eye diseases.

Genet Epidemiol 2021 07 1;45(5):455-470. Epub 2021 Mar 1.

Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA.

Genetic studies of two related survival outcomes of a pleiotropic gene are commonly encountered but statistical models to analyze them are rarely developed. To analyze sequencing data, we propose mixed effect Cox proportional hazard models by functional regressions to perform gene-based joint association analysis of two survival traits motivated by our ongoing real studies. These models extend fixed effect Cox models of univariate survival traits by incorporating variations and correlation of multivariate survival traits into the models. The associations between genetic variants and two survival traits are tested by likelihood ratio test statistics. Extensive simulation studies suggest that type I error rates are well controlled and power performances are stable. The proposed models are applied to analyze bivariate survival traits of left and right eyes in the age-related macular degeneration progression.
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http://dx.doi.org/10.1002/gepi.22381DOI Listing
July 2021

Deep-learning based multi-modal retinal image registration for the longitudinal analysis of patients with age-related macular degeneration.

Biomed Opt Express 2021 Jan 23;12(1):619-636. Epub 2020 Dec 23.

National Eye Institute, National Institutes of Health, Bethesda, MD 20892, USA.

This work reports a deep-learning based registration algorithm that aligns multi-modal retinal images collected from longitudinal clinical studies to achieve accuracy and robustness required for analysis of structural changes in large-scale clinical data. Deep-learning networks that mirror the architecture of conventional feature-point-based registration were evaluated with different networks that solved for registration affine parameters, image patch displacements, and patch displacements within the region of overlap. The ground truth images for deep learning-based approaches were derived from successful conventional feature-based registration. Cross-sectional and longitudinal affine registrations were performed across color fundus photography (CFP), fundus autofluorescence (FAF), and infrared reflectance (IR) image modalities. For mono-modality longitudinal registration, the conventional feature-based registration method achieved mean errors in the range of 39-53 µm (depending on the modality) whereas the deep learning method with region overlap prediction exhibited mean errors in the range 54-59 µm. For cross-sectional multi-modality registration, the conventional method exhibited gross failures with large errors in more than 50% of the cases while the proposed deep-learning method achieved robust performance with no gross failures and mean errors in the range 66-69 µm. Thus, the deep learning-based method achieved superior overall performance across all modalities. The accuracy and robustness reported in this work provide important advances that will facilitate clinical research and enable a detailed study of the progression of retinal diseases such as age-related macular degeneration.
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http://dx.doi.org/10.1364/BOE.408573DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818952PMC
January 2021

Retrobulbar Hemangioblastomas in von Hippel-Lindau Disease: Clinical Course and Management.

Neurosurgery 2021 04;88(5):1012-1020

Neurosurgery Unit for Pituitary and Inheritable Diseases, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland.

Background: Retrobulbar hemangioblastomas involving the optic apparatus in patients with von Hippel-Lindau disease (VHL) are rare, with only 25 reported cases in the literature.

Objective: To analyze the natural history of retrobulbar hemangioblastomas in a large cohort of VHL patients in order to define presentation, progression, and management.

Methods: Clinical history and imaging of 250 patients with VHL in an ongoing natural history trial and 1774 patients in a neurosurgical protocol were reviewed. The clinical course, magnetic resonance images, treatment, and outcomes were reviewed for all included patients.

Results: A total of 18 patients with retrobulbar hemangioblastoma on surveillance magnetic resonance imaging met the inclusion criteria for this study. Of the 17 for whom clinical information was available, 10 patients presented with symptoms related to the hemangioblastoma, and 7 were asymptomatic. The mean tumor volume was larger for symptomatic (810.6 ± 545.5 mm3) compared to asymptomatic patients (307.6 ± 245.5 mm3; P < .05). A total of 5 of the symptomatic patients were treated surgically and all experienced improvement in their symptoms. All 3 symptomatic patients that did not undergo intervention had continued symptom progression. Long-term serial imaging on asymptomatic patients showed that these tumors can remain radiographically stable and asymptomatic for extended periods of time (101.43 ± 71 mo).

Conclusion: This study suggests that retrobulbar hemangioblastomas may remain stable and clinically asymptomatic for long durations. Recent growth and larger tumor volume were associated with symptom occurrence. Surgical treatment of symptomatic retrobulbar hemangioblastomas can be safe and may reverse the associated symptoms.
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http://dx.doi.org/10.1093/neuros/nyaa565DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8223245PMC
April 2021

Automated Quantitative Assessment of Retinal Fluid Volumes as Important Biomarkers in Neovascular Age-Related Macular Degeneration.

Am J Ophthalmol 2021 04 15;224:267-281. Epub 2021 Feb 15.

Department of Ophthalmology and Optometry, Christian Doppler Laboratory for Ophthalmic Image Analyses (OPTIMA), Medical University of Vienna, Vienna, Austria.

Purpose: To evaluate retinal fluid volume data extracted from optical coherence tomography (OCT) scans by artificial intelligence algorithms in the treatment of neovascular age-related macular degeneration (NV-AMD).

Design: Perspective.

Methods: A review was performed of retinal image repository datasets from diverse clinical settings.

Settings: Clinical trial (HARBOR) and trial follow-on (Age-Related Eye Disease Study 2 10-year Follow-On); real-world (Belfast and Tel-Aviv tertiary centers).

Patients: 24,362 scans of 1,095 eyes (HARBOR); 4,673 of 880 (Belfast); 1,470 of 132 (Tel-Aviv); 511 of 511 (Age-Related Eye Disease Study 2 10-year Follow-On). ObservationProcedures: Vienna Fluid Monitor or Notal OCT Analyzer applied to macular cube scans. OutcomeMeasures: Intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) volumes.

Results: The fluid volumes measured in neovascular AMD were expressed efficiently in nanoliters. Large ranges that differed by population were observed at the treatment-naïve stage: 0-3,435 nL (IRF), 0-5,018 nL (SRF), and 0-10,022 nL (PED). Mean volumes decreased rapidly and consistently with anti-vascular endothelial growth factor therapy. During maintenance therapy, mean IRF volumes were highest in Tel-Aviv (100 nL), lower in Belfast and HARBOR-Pro Re Nata, and lowest in HARBOR-monthly (21 nL). Mean SRF volumes were low in all: 30 nL (HARBOR-monthly) and 48-49 nL (others).

Conclusions: Quantitative measures of IRF, SRF, and PED are important biomarkers in NV-AMD. Accurate volumes can be extracted efficiently from OCT scans by artificial intelligence algorithms to guide the treatment of exudative macular diseases. Automated fluid monitoring identifies fluid characteristics in different NV-AMD populations at baseline and during follow-up. For consistency between studies, we propose the nanoliter as a convenient unit. We explore the advantages of using these quantitative metrics in clinical practice and research.
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http://dx.doi.org/10.1016/j.ajo.2020.12.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8058226PMC
April 2021

Local Anatomic Precursors to New-Onset Geographic Atrophy in Age-Related Macular Degeneration as Defined on OCT.

Ophthalmol Retina 2021 05 22;5(5):396-408. Epub 2020 Dec 22.

Duke Eye Center, Duke University Medical Center, Durham, North Carolina; Department of Biomedical Engineering, The Pratt School of Engineering, Duke University, Durham, North Carolina. Electronic address:

Purpose: In macula-wide analyses, spectral-domain (SD) optical coherence tomography (OCT) features including drusen volume, hyperreflective foci, and OCT-reflective drusen substructures independently predict geographic atrophy (GA) onset secondary to age-related macular degeneration (AMD). We sought to identify SD OCT features in the location of new GA before its onset.

Design: Retrospective study.

Participants: Age-Related Eye Disease Study 2 Ancillary SD OCT Study participants.

Methods: We analyzed longitudinally captured SD OCT images and color photographs from 488 eyes of 488 participants with intermediate AMD at baseline. Sixty-two eyes with sufficient image quality demonstrated new-onset GA on color photographs during study years 2 through 7. The area of new-onset GA and one size-matched control region in the same eye were segmented separately, and corresponding spatial volumes on registered SD OCT images at the GA incident year and at 2, 3, and 4 years previously were defined. Differences in SD OCT features between paired precursor regions were evaluated through matched-pairs analyses.

Main Outcome Measures: Localized SD OCT features 2 years before GA onset.

Results: Compared with paired control regions, GA precursor regions at 2, 3, and 4 years before (n = 54, 33, and 25, respectively) showed greater drusen volume (P = 0.01, P = 0.003, and P = 0.003, respectively). At 2 and 3 years before GA onset, they were associated with the presence of hypertransmission (P < 0.001 and P = 0.03, respectively), hyperreflective foci (P < 0.001 and P = 0.045, respectively), OCT-reflective drusen substructures (P = 0.004 and P = 0.03, respectively), and loss or disruption of the photoreceptor zone, ellipsoid zone, and retinal pigment epithelium (RPE, P < 0.001 and P = 0.005-0.045, respectively). At 4 years before GA onset, precursor regions were associated with photoreceptor zone thinning (P = 0.007) and interdigitation zone loss (P = 0.045).

Conclusions: Evolution to GA is heralded by early local photoreceptor changes and drusen accumulation, detectable 4 years before GA onset. These precede other anatomic heralds such as RPE changes and drusen substructure emergence detectable 1 to 2 years before GA. This study thus identified earlier end points for GA as potential therapeutic targets in clinical trials.
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http://dx.doi.org/10.1016/j.oret.2020.12.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8362836PMC
May 2021

Common variants in SOX-2 and congenital cataract genes contribute to age-related nuclear cataract.

Commun Biol 2020 12 11;3(1):755. Epub 2020 Dec 11.

Institute of Molecular and Cell Biology, 138673, Singapore, Singapore.

Nuclear cataract is the most common type of age-related cataract and a leading cause of blindness worldwide. Age-related nuclear cataract is heritable (h = 0.48), but little is known about specific genetic factors underlying this condition. Here we report findings from the largest to date multi-ethnic meta-analysis of genome-wide association studies (discovery cohort N = 14,151 and replication N = 5299) of the International Cataract Genetics Consortium. We confirmed the known genetic association of CRYAA (rs7278468, P = 2.8 × 10) with nuclear cataract and identified five new loci associated with this disease: SOX2-OT (rs9842371, P = 1.7 × 10), TMPRSS5 (rs4936279, P = 2.5 × 10), LINC01412 (rs16823886, P = 1.3 × 10), GLTSCR1 (rs1005911, P = 9.8 × 10), and COMMD1 (rs62149908, P = 1.2 × 10). The results suggest a strong link of age-related nuclear cataract with congenital cataract and eye development genes, and the importance of common genetic variants in maintaining crystalline lens integrity in the aging eye.
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http://dx.doi.org/10.1038/s42003-020-01421-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7733496PMC
December 2020

Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2.

Br J Ophthalmol 2020 Nov 23. Epub 2020 Nov 23.

Biomedical Engineering, Duke University, Durham, North Carolina, USA.

Aim: To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2).

Methods: The dataset consisted of 99 eyes from 67 participants enrolled in an international, multicentre, phase 2 MacTel2 clinical trial (NCT01949324). Each eye was imaged with spectral-domain OCT at three time points over 2 years. Retinal cavitations were manually segmented by a trained Reader and the retinal cavitation volume was calculated. Two convolutional neural networks (CNNs) were developed that operated in sequential stages. In the first stage, CNN1 classified whether a B-scan contained any retinal cavitations. In the second stage, CNN2 segmented the retinal cavitations in a B-scan. We evaluated the performance of the proposed method against alternative methods using several performance metrics and manual segmentations as the gold standard.

Results: The proposed method was computationally efficient and accurately classified and segmented retinal cavitations on OCT images, with a sensitivity of 0.94, specificity of 0.80 and average Dice similarity coefficient of 0.94±0.07 across all time points. The proposed method produced measurements that were highly correlated with the manual measurements of retinal cavitation volume and change in retinal cavitation volume over time.

Conclusion: The proposed method will be useful to help clinicians quantify retinal cavitations, assess changes over time and further investigate the clinical significance of these early structural changes observed in MacTel2.
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http://dx.doi.org/10.1136/bjophthalmol-2020-317131DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8365558PMC
November 2020

A recommended "minimum data set" framework for SD-OCT retinal image acquisition and analysis from the Atlas of Retinal Imaging in Alzheimer's Study (ARIAS).

Alzheimers Dement (Amst) 2020 1;12(1):e12119. Epub 2020 Nov 1.

Department of Biomedical and Pharmaceutical Sciences University of Rhode Island Kingston Rhode Island USA.

Introduction: We propose a minimum data set framework for the acquisition and analysis of retinal images for the development of retinal Alzheimer's disease (AD) biomarkers. Our goal is to describe methodology that will increase concordance across laboratories, so that the broader research community is able to cross-validate findings in parallel, accumulate large databases with normative data across the cognitive aging spectrum, and progress the application of this technology from the discovery stage to the validation stage in the search for sensitive and specific retinal biomarkers in AD.

Methods: The proposed minimum data set framework is based on the Atlas of Retinal Imaging Study (ARIAS), an ongoing, longitudinal, multi-site observational cohort study. However, the ARIAS protocol has been edited and refined with the expertise of all co-authors, representing 16 institutions, and research groups from three countries, as a first step to address a pressing need identified by experts in neuroscience, neurology, optometry, and ophthalmology at the Retinal Imaging in Alzheimer's Disease (RIAD) conference, convened by the Alzheimer's Association and held in Washington, DC, in May 2019.

Results: Our framework delineates specific imaging protocols and methods of analysis for imaging structural changes in retinal neuronal layers, with optional add-on procedures of fundus autofluorescence to examine beta-amyloid accumulation and optical coherence tomography angiography to examine AD-related changes in the retinal vasculature.

Discussion: This minimum data set represents a first step toward the standardization of retinal imaging data acquisition and analysis in cognitive aging and AD. A standardized approach is essential to move from discovery to validation, and to examine which retinal AD biomarkers may be more sensitive and specific for the different stages of the disease severity spectrum. This approach has worked for other biomarkers in the AD field, such as magnetic resonance imaging; amyloid positron emission tomography; and, more recently, blood proteomics. Potential context of use for retinal AD biomarkers is discussed.
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http://dx.doi.org/10.1002/dad2.12119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7604454PMC
November 2020
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