Randomized 20-year infancy-onset dietary intervention, life-long cardiovascular risk factors and retinal microvasculature.

BACKGROUND AND AIMS: Retinal microvasculature characteristics predict cardiovascular morbidity and mortality. This study investigated associations of lifelong cardiovascular risk factors and effects of dietary intervention on retinal microvasculature in young adulthood. METHODS: The cohort is derived from the longitudinal Special Turku Coronary Risk Factor Intervention Project study. The Special Turku Coronary Risk Factor Intervention Project is a 20-year infancy-onset randomized controlled dietary intervention study with frequent study visits and follow-up extending to age 26 years. The dietary intervention aimed at a heart-healthy diet. Fundus photographs were taken at the 26-year follow-up, and microvascular measures [arteriolar and venular diameters, tortuosity (simple and curvature) and fractal dimensions] were derived (n = 486). Cumulative exposure as the area under the curve for cardiovascular risk factors and dietary components was determined for the longest available time period (e.g. from age 7 months to 26 years). RESULTS: The dietary intervention had a favourable effect on retinal microvasculature resulting in less tortuous arterioles and venules and increased arteriolar fractal dimension in the intervention group when compared with the control group. The intervention effects were found even when controlled for the cumulative cardiovascular risk factors. Reduced lifelong cumulative intake of saturated fats, main target of the intervention, was also associated with less tortuous venules. Several lifelong cumulative risk factors were independently associated with the retinal microvascular measures, e.g. cumulative systolic blood pressure with narrower arterioles. CONCLUSIONS: Infancy-onset 20-year dietary intervention had favourable effects on the retinal microvasculature in young adulthood. Several lifelong cumulative cardiovascular risk factors were independently associated with retinal microvascular structure.

Effects of firsthand tobacco smoking on retinal vessel caliber: a systematic review and meta-analysis.

PURPOSE: To review the effects of firsthand tobacco smoking on central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE) of firsthand tobacco smokers. METHODS: We performed a search on EMBASE and PubMed for studies up to 15th July 2022. Two independent reviewers selected studies with baseline data of CRAE and CRVE of current smokers, nonsmokers, and former smokers. Initial search identified 893 studies, of which 10 were included in the meta-analysis. Two independent reviewers extracted data from the included studies. The quality of studies was assessed by the Newcastle-Ottawa Scale. RESULTS: In this meta-analysis, 7431 nonsmokers, 2448 current smokers and 5786 former smokers, as well as 7404 nonsmokers, 2430 current smokers and 5763 former smokers were included in CRAE and CRVE analysis respectively. Nonsmokers had narrower CRVE (Weighted mean difference [WMD], -12.15; 95% CI, -17.33 - -6.96) and CRAE (WMD, -4.77; 95% CI, -7.96 - -1.57) than current smokers, and narrower CRVE (WMD, -3.08; 95% CI, -6.06 - -0.11) than former smokers. Current smokers had wider CRVE (WMD, 10.42; 95% CI, 7.80 - 13.04) and CRAE (WMD, 7.05; 95% CI, 6.65 - 7.46) than former smokers. Subgroup analysis and sensitivity analysis were performed. CONCLUSION: Firsthand tobacco smoking resulted in wider CRAE and CRVE in current and former smokers, particularly in CRVE, and such changes may not be reversible after smoking cessation. Therefore, retinal vessel caliber may reflect the effects of firsthand tobacco smoking and be used to estimate the risk of cardiovascular diseases.

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied. METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS: The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS: A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke-NUS Ophthalmology and Visual Sciences Academic Clinical Program.).

High Myopia Normative Database of Peripapillary Retinal Nerve Fiber Layer Thickness to Detect Myopic Glaucoma in a Chinese Population.

PURPOSE: To develop and validate the performance of a high myopia (HM)-specific normative database of peripapillary retinal nerve fiber layer (pRNFL) thickness in differentiating HM from highly myopic glaucoma (HMG). DESIGN: Cross-sectional multicenter study. PARTICIPANTS: A total of 1367 Chinese participants (2325 eyes) with nonpathologic HM or HMG were included from 4 centers. After quality control, 1108 eyes from 694 participants with HM were included in the normative database; 459 eyes from 408 participants (323 eyes with HM and 136 eyes with HMG) and 322 eyes from 197 participants (131 eyes with HM and 191 eyes with HMG) were included in the internal and external validation sets, respectively. Only HMG eyes with an intraocular pressure > 21 mmHg were included. METHODS: The pRNFL thickness was measured with swept-source (SS) OCT. Four strategies of pRNFL-specified values were examined, including global and quadrantic pRNFL thickness below the lowest fifth or the lowest first percentile of the normative database. MAIN OUTCOMES MEASURES: The accuracy, sensitivity, and specificity of the HM-specific normative database for detecting HMG. RESULTS: Setting the fifth percentile of the global pRNFL thickness as the threshold, using the HM-specific normative database, we achieved an accuracy of 0.93 (95% confidence interval [CI], 0.90-0.95) and 0.85 (95% CI, 0.81-0.89), and, using the first percentile as the threshold, we acheived an accuracy of 0.85 (95% CI, 0.81-0.88) and 0.70 (95% CI, 0.65-0.75) in detecting HMG in the internal and external validation sets, respectively. The fifth percentile of the global pRNFL thickness achieved high sensitivities of 0.75 (95% CI, 0.67-0.82) and 0.75 (95% CI, 0.68-0.81) and specificities of 1.00 (95% CI, 0.99-1.00) and 1.00 (95% CI, 0.97-1.00) in the internal and external validation datasets, respectively. Compared with the built-in database of the OCT device, the HM-specific normative database showed a higher sensitivity and specificity than the corresponding pRNFL thickness below the fifth or first percentile (P < 0.001 for all). CONCLUSIONS: The HM-specific normative database is more capable of detecting HMG eyes than the SS OCT built-in database, which may be an effective tool for differential diagnosis between HMG and HM. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

COMPARING FUNCTIONAL AND VASCULAR LAYER OUTCOMES OF LASER PHOTOCOAGULATION VERSUS SUBTHRESHOLD MICROPULSE LASER FOR DIABETIC MACULAR EDEMA: An OCT-Angiography Study.

PURPOSE: To compare the efficacy of conventional laser and subthreshold micropulse laser (SML) in treating diabetic macular edema in terms of functional outcomes and changes in quantitative metrics for the retinal capillary and choriocapillary vascular layers. METHODS: Fifty-two eyes from 52 patients with treatment-naive, clinically significant macular edema were randomly assigned to the conventional laser group or SML group in a 1:1 ratio. Best-corrected visual acuity, central macular thickness (CMT), and optical coherence tomography angiography scans were measured at baseline, 1, 3, and 6 months after treatment. RESULTS: The SML group showed rapid visual recovery, improving from baseline of 0.320 ± 0.31 logarithm of the minimum angle of resolution (20/42 Snellen) to 0.270 ± 0.22 logarithm of the minimum angle of resolution (20/37 Snellen) at 1 month ( P = 0.038) and had significant improvements in CMT at 6-month post-treatment (353.88-301.00 µ m, P = 0.005). Statistically significant changes were detected across all optical coherence tomography angiography metrics, including vessel density, vessel length density, vessel diameter index, and fractal dimension, at 6 months for both groups in the deep capillary plexus and choriocapillary plexus. CONCLUSION: Subthreshold micropulse laser resulted in early visual recovery and sustained macular thickness improvement in the treatment of diabetic macular edema. Microvascular perfusion parameters, including vessel density, vessel length density, and fractal dimension, improved in the deep capillary plexus and choriocapillary plexus for both treatment groups at 6 months post-treatment.

Deep learning in optical coherence tomography: Where are the gaps?

Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.

Retinal vascular profile in predicting incident cardiometabolic diseases among individuals with diabetes.

OBJECTIVE: To determine the longitudinal associations between retinal vascular profile (RVP) and four major cardiometabolic diseases; and to quantify the predictive improvements when adding RVP beyond traditional risk factors in individuals with diabetes. METHODS: Subjects were enrolled from the Singapore Epidemiology of Eye Disease (SEED) study, a multi-ethnic population-based cohort. Four incident cardiometabolic diseases, calculated over a ~ 6-year period, were considered: cardiovascular disease (CVD), hypertension (HTN), diabetic kidney disease (DKD), and hyperlipidemia (HLD). The RVP-vessel tortuosity, branching angle, branching coefficient, fractal dimension, vessel caliber, and DR status-was characterized at baseline using a computer-assisted program. Traditional risk factors at baseline included age, gender, ethnicity, smoking, blood pressure (BP), HbA1c, estimated glomerular filtration rate (eGFR), or cholesterol. The improvements in predictive performance when adding RVP (compared with only traditional risk factors) was calculated using several metrics including area under the receiver operating characteristics curve (AUC) and net reclassification improvement (NRI). RESULTS: Among 1770 individuals with diabetes, incidences were 6.3% (n = 79/1259) for CVD, 48.7% (n = 166/341) for HTN, 14.6% (n = 175/1199) for DKD, and 59.4% (n = 336/566) for HLD. DR preceded the onset of CVD (RR 1.85[1.14;3.00]) and DKD (1.44 [1.06;1.96]). Narrower arteriolar caliber preceding the onset of HTN (0.84 [0.72;0.99]), and changes in arteriolar branching angle preceded the onset of CVD (0.78 [0.62;0.98]) and HTN (1.15 [1.03;1.29]). The largest predictive improvement was found for HTN with AUC increment of 3.4% (p = .027) and better reclassification of 11.4% of the cases and 4.6% of the controls (p = .008). CONCLUSION: We found that RVPs improved the prediction of HTN in individuals with diabetes, but add limited information for CVD, DKD, and HLD predictions.

Artificial Intelligence for Retinopathy of Prematurity: Validation of a Vascular Severity Scale against International Expert Diagnosis.

PURPOSE: To validate a vascular severity score as an appropriate output for artificial intelligence (AI) Software as a Medical Device (SaMD) for retinopathy of prematurity (ROP) through comparison with ordinal disease severity labels for stage and plus disease assigned by the International Classification of Retinopathy of Prematurity, Third Edition (ICROP3), committee. DESIGN: Validation study of an AI-based ROP vascular severity score. PARTICIPANTS: A total of 34 ROP experts from the ICROP3 committee. METHODS: Two separate datasets of 30 fundus photographs each for stage (0-5) and plus disease (plus, preplus, neither) were labeled by members of the ICROP3 committee using an open-source platform. Averaging these results produced a continuous label for plus (1-9) and stage (1-3) for each image. Experts were also asked to compare each image to each other in terms of relative severity for plus disease. Each image was also labeled with a vascular severity score from the Imaging and Informatics in ROP deep learning system, which was compared with each grader's diagnostic labels for correlation, as well as the ophthalmoscopic diagnosis of stage. MAIN OUTCOME MEASURES: Weighted kappa and Pearson correlation coefficients (CCs) were calculated between each pair of grader classification labels for stage and plus disease. The Elo algorithm was also used to convert pairwise comparisons for each expert into an ordered set of images from least to most severe. RESULTS: The mean weighted kappa and CC for all interobserver pairs for plus disease image comparison were 0.67 and 0.88, respectively. The vascular severity score was found to be highly correlated with both the average plus disease classification (CC = 0.90, P < 0.001) and the ophthalmoscopic diagnosis of stage (P < 0.001 by analysis of variance) among all experts. CONCLUSIONS: The ROP vascular severity score correlates well with the International Classification of Retinopathy of Prematurity committee member's labels for plus disease and stage, which had significant intergrader variability. Generation of a consensus for a validated scoring system for ROP SaMD can facilitate global innovation and regulatory authorization of these technologies.

Retinal Nerve Fiber Layer Thickness and Rim Area Profiles in Asians: Pooled Analysis from the Asian Eye Epidemiology Consortium.

PURPOSE: To evaluate ethnic variations, ocular and systemic determinants of retinal nerve fiber layer (RNFL) thickness, and neuroretinal rim area among Asians using a large consortium of population-based eye studies. DESIGN: Cross-sectional pooled analysis. PARTICIPANTS: Twenty-two thousand four hundred thirty-six participants (22 436 eyes) from 10 population-based studies (in China, Hong Kong, India, Japan, Russia, and Singapore) of the Asian Eye Epidemiology Consortium. METHODS: Participants 40 years of age or older without glaucoma were included. All participants underwent spectral-domain OCT imaging and systemic and ocular examinations. Data were pooled from each study. Multivariable regression was performed to evaluate interethnic differences, intermachine variations, and ocular and systemic factors associated with RNFL thickness and rim area, adjusting for age, gender, diabetes, intraocular pressure (IOP), spherical equivalent (SE), ethnicity, OCT model, and study group. When evaluating body mass index, smoking, and hypertension as exposures, these factors were additionally adjusted for in the model. MAIN OUTCOME MEASURES: Average RNFL thickness (in micrometers) and rim area (in square millimeters). RESULTS: Indian and Japanese eyes have thinner RNFLs than those of other Asian ethnicities (ß values range, 7.31-12.76 µm; P < 0.001 for all pairwise comparisons). Compared with measurements by Cirrus HD-OCT (Carl Zeiss Meditec, Inc), RNFL on average was 7.29 µm thicker when measured by Spectralis (Heidelberg Engineering), 12.85 µm thicker when measured by RS-3000 (NIDEK Co, Ltd), and 17.48 µm thicker when measured by iVue/RTVue (Optovue, Inc) devices (all P < 0.001). Additionally, older age (per decade, ß = -2.70), diabetes (ß = -0.72), higher IOP (per 1 mmHg, ß = -0.07), more myopic SE (per diopter, ß = -1.13), cardiovascular disease (ß = -0.94), and hypertension (ß = -0.68) were associated with thinner RNFL (all P ≤ 0.003). Similarly, older age (ß = -0.019), higher IOP (ß = -0.010), and more myopic SE (ß = -0.025) were associated with smaller rim area (all P < 0.001). CONCLUSIONS: In this large pooled analysis of Asian population studies, Indian and Japanese eyes were observed to have thinner RNFL profiles. These findings suggest the need for an ethnic-specific normative database to improve glaucoma detection.

Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions.

PURPOSE OF REVIEW: Retinal microvasculature assessment has shown promise to enhance cardiovascular disease (CVD) risk stratification. Integrating artificial intelligence into retinal microvasculature analysis may increase the screening capacity of CVD risks compared with risk score calculation through blood-taking. This review summarizes recent advancements in artificial intelligence based retinal photograph analysis for CVD prediction, and suggests challenges and future prospects for translation into a clinical setting. RECENT FINDINGS: Artificial intelligence based retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes), direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD biomarkers (e.g. coronary artery calcium score). However, challenges such as handling photographs with concurrent retinal diseases, limited diverse data from other populations or clinical settings, insufficient interpretability and generalizability, concerns on cost-effectiveness and social acceptance may impede the dissemination of these artificial intelligence algorithms into clinical practice. SUMMARY: Artificial intelligence based retinal microvasculature analysis may supplement existing CVD risk stratification approach. Although technical and socioeconomic challenges remain, we envision artificial intelligence based microvasculature analysis to have major clinical and research impacts in the future, through screening for high-risk individuals especially in less-developed areas and identifying new retinal biomarkers for CVD research.

A MULTITASK DEEP-LEARNING SYSTEM FOR ASSESSMENT OF DIABETIC MACULAR ISCHEMIA ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGES.

PURPOSE: We aimed to develop and test a deep-learning system to perform image quality and diabetic macular ischemia (DMI) assessment on optical coherence tomography angiography (OCTA) images. METHODS: This study included 7,194 OCTA images with diabetes mellitus for training and primary validation and 960 images from three independent data sets for external testing. A trinary classification for image quality assessment and the presence or absence of DMI for DMI assessment were labeled on all OCTA images. Two DenseNet-161 models were built for both tasks for OCTA images of superficial and deep capillary plexuses, respectively. External testing was performed on three unseen data sets in which one data set using the same model of OCTA device as of the primary data set and two data sets using another brand of OCTA device. We assessed the performance by using the area under the receiver operating characteristic curves with sensitivities, specificities, and accuracies and the area under the precision-recall curves with precision. RESULTS: For the image quality assessment, analyses for gradability and measurability assessment were performed. Our deep-learning system achieved the area under the receiver operating characteristic curves >0.948 and area under the precision-recall curves >0.866 for the gradability assessment, area under the receiver operating characteristic curves >0.960 and area under the precision-recall curves >0.822 for the measurability assessment, and area under the receiver operating characteristic curves >0.939 and area under the precision-recall curves >0.899 for the DMI assessment across three external validation data sets. Grad-CAM demonstrated the capability of our deep-learning system paying attention to regions related to DMI identification. CONCLUSION: Our proposed multitask deep-learning system might facilitate the development of a simplified assessment of DMI on OCTA images among individuals with diabetes mellitus at high risk for visual loss.

Retinal imaging in Alzheimer's disease.

Identifying biomarkers of Alzheimer's disease (AD) will accelerate the understanding of its pathophysiology, facilitate screening and risk stratification, and aid in developing new therapies. Developments in non-invasive retinal imaging technologies, including optical coherence tomography (OCT), OCT angiography and digital retinal photography, have provided a means to study neuronal and vascular structures in the retina in people with AD. Both qualitative and quantitative measurements from these retinal imaging technologies (eg, thinning of peripapillary retinal nerve fibre layer, inner retinal layer, and choroidal layer, reduced capillary density, abnormal vasodilatory response) have been shown to be associated with cognitive function impairment and risk of AD. The development of computer algorithms for respective retinal imaging methods has further enhanced the potential of retinal imaging as a viable tool for rapid, early detection and screening of AD. In this review, we present an update of current retinal imaging techniques and their potential applications in AD research. We also discuss the newer retinal imaging techniques and future directions in this expanding field.

Vision, vision-specific functioning and mobility, and their relationship with clinically assessed cognitive impairment.

BACKGROUND: The relationship between self-reported visual disability and cognitive impairment in older individuals is unclear. OBJECTIVE: To determine the relationship of vision-specific functioning (VSF), vision-specific mobility (VSM) and visual acuity (VA) with clinically assessed cognitive impairment in the Epidemiology of Dementia in Singapore study. DESIGN: Cross-sectional. SETTING: Population-based. SUBJECTS: Eight hundred and seventy-four adults aged ≥60 years at higher risk of possible cognitive impairment by the Abbreviated Mental Test and progressive forgetfulness question. METHODS: VSF and VSM were measured using Rasch-transformed continuous scores of two Impact of Vision Impairment questionnaire domains. Cognitive impairment was objectively determined using detailed neuropsychological testing and defined as no cognitive impairment (NCI), mild cognitive impairment-no dementia (CIND), moderate CIND only and moderate CIND or dementia. Associations were assessed using multinomial logistic regression models. RESULTS: Of the 874 participants (49.0% males, mean age (SD) 65.5 (7.0) years), 277, 281 and 316 had NCI, mild CIND and moderate CIND or dementia, respectively. Compared to NCI, the odds of moderate CIND, and moderate CIND or dementia increased for every SD worsening in VSF (OR: 1.44, 95% CI 1.14-1.82, and OR: 1.52, 95%CI 1.19-1.94, respectively) and VSM (OR: 1.42, 95%CI 1.11-1.81, and OR: 1.50, 95%CI 1.15-1.95). Similarly, the odds of mild CIND (OR: 1.62, 95%CI 1.19-2.22), moderate CIND (OR: 1.93, 95%CI 1.45-2.58), and moderate CIND or dementia (OR: 2.25, 95%CI 1.62-3.11) increased significantly with every SD worsening of VA. CONCLUSIONS: Our results emphasise the importance of interventions to prevent vision loss and improve quality of life to reduce likelihood of age-related cognitive decline.

Comparison of Peripapillary Vessel Density of Acute Nonarteritic Anterior Ischemic Optic Neuropathy and Other Optic Neuropathies With Disc Swelling Using Optical Coherence Tomography Angiography: A Pilot Study.

BACKGROUND: The purpose of this study is to quantitatively compare the peripapillary vessel density (PPVD), measured with optical coherence tomography angiography (OCT-A), between acute nonarteritic anterior ischemic optic neuropathy (NAION) and other causes of disc swelling ("others"). METHODS: In this prospective comparative case series, patients with unilateral disc swelling due to acute NAION (n = 7) and "others" (n = 7) underwent OCT-A scanning of the optic nerve head with a swept-source OCT (Triton DRI-OCT), in addition to functional assessment. OCT-A images were analyzed using an automated customized MATLAB program. Comparison was made between total and 6 sectoral PPVD (radial peripapillary capillary [RPC] and choroid layers) of affected and fellow eyes; and between the 2 groups' affected eyes. Five NAION patients had repeated assessments at 1, 3, and 6 months. RESULTS: Acute NAION eyes had a significantly lower total and superonasal PPVD (both layers) compared to fellow eyes. No such difference was observed in "others" group for the RPC layer. NAION eyes also had significantly lower total RPC PPVD than affected eyes in the "others" group. Over 6 months, NAION eyes had persistently lower RPC PPVD compared to fellow eyes but the reduced choroidal PPVD resolved by 1 month. CONCLUSION: The study demonstrated reduced superonasal and total RPC PPVD in acute NAION, which persisted over 6 months. Because there is currently no single diagnostic test for NAION, use of OCT-A images to analyze RPC PPVD may potentially help distinguish acute NAION from other causes of disc swelling by quantitatively demonstrating capillary dropout in the RPC layer.

Profiles of Ganglion Cell-Inner Plexiform Layer Thickness in a Multi-Ethnic Asian Population: The Singapore Epidemiology of Eye Diseases Study.

PURPOSE: To examine the normative profile and determinants of macular ganglion cell-inner plexiform layer (GCIPL) thickness based on spectral-domain OCT (SD-OCT) in a nonglaucoma, multi-ethnic Asian population. DESIGN: Population-based, cross-sectional study. PARTICIPANTS: Ethnic Chinese, Malay, and Indian adults aged ≥40 years recruited from the Singapore Epidemiology of Eye Diseases Study. METHODS: All participants underwent standardized examinations. The GCIPL thickness was measured using Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA). Participants with glaucoma or poor-quality scans were excluded. Eye-specific data were used. Associations of ocular and systemic factors with GCIPL thickness parameters were investigated using multivariable linear regression with generalized estimating equation models to account for correlation between both eyes. MAIN OUTCOME MEASURES: GCIPL thickness. RESULTS: A total of 4464 participants (7520 eyes) consisting of 1625 Chinese, 1212 Malay, and 1627 Indian adults contributed to this analysis. Average GCIPL thickness was 82.6±6.1 µm in Chinese, 81.5±6.8 µm in Malays, and 78.0±6.9 µm in Indians (P < 0.001 by analysis of variance). The 5th percentile limit of average GCIPL thickness was 72 µm in Chinese, 70 µm in Malays, and 67 µm in Indians. In multivariable analysis adjusting for age, gender, axial length, presence of cataract, OCT signal strength, disc area, hypertension, diabetes, and hyperlipidemia, eyes of Indians were observed to have 3.43 µm thinner GCIPL on average compared with Chinese (P < 0.001) and 3.36 µm thinner GCIPL compared with Malays (P < 0.001). In addition, older age (per decade; ß = -2.51), female (ß = -1.57), longer axial length (per mm; ß = -1.54), and presence of chronic kidney disease (ß = -1.49) were significantly associated with thinner average GCIPL (all P ≤ 0.008). Larger optic disc area (per mm2; ß = 0.78; P < 0.001) was associated with thicker GCIPL. These factors were consistently observed to be significant for superior and inferior hemisphere GCIPL thickness. CONCLUSIONS: GCIPL thickness profiles were significantly thinner in Indians compared with Chinese and Malays. Our findings further highlight the need of a more refined, ethnic-specific normative database for GCIPL thickness, which in turn may improve the detection and diagnosis of glaucoma in Asians.

Retinal Vascular Signs and Cerebrovascular Diseases.

BACKGROUND: Cerebrovascular disease (CeVD), including stroke, is a leading cause of death globally. The retina is an extension of the cerebrum, sharing embryological and vascular pathways. The association between different retinal signs and CeVD has been extensively evaluated. In this review, we summarize recent studies which have examined this association. EVIDENCE ACQUISITION: We searched 6 databases through July 2019 for studies evaluating the link between retinal vascular signs and diseases with CeVD. CeVD was classified into 2 groups: clinical CeVD (including clinical stroke, silent cerebral infarction, cerebral hemorrhage, and stroke mortality), and sub-clinical CeVD (including MRI-defined lacunar infarct and white matter lesions [WMLs]). Retinal vascular signs were classified into 3 groups: classic hypertensive retinopathy (including retinal microaneurysms, retinal microhemorrhage, focal/generalized arteriolar narrowing, cotton-wool spots, and arteriovenous nicking), clinical retinal diseases (including diabetic retinopathy [DR], age-related macular degeneration [AMD], retinal vein occlusion, retinal artery occlusion [RAO], and retinal emboli), and retinal vascular imaging measures (including retinal vessel diameter and geometry). We also examined emerging retinal vascular imaging measures and the use of artificial intelligence (AI) deep learning (DL) techniques. RESULTS: Hypertensive retinopathy signs were consistently associated with clinical CeVD and subclinical CeVD subtypes including subclinical cerebral large artery infarction, lacunar infarction, and WMLs. Some clinical retinal diseases such as DR, retinal arterial and venous occlusion, and transient monocular vision loss are consistently associated with clinical CeVD. There is an increased risk of recurrent stroke immediately after RAO. Less consistent associations are seen with AMD. Retinal vascular imaging using computer assisted, semi-automated software to measure retinal vascular caliber and other parameters (tortuosity, fractal dimension, and branching angle) has shown strong associations to clinical and subclinical CeVD. Other new retinal vascular imaging techniques (dynamic retinal vessel analysis, adaptive optics, and optical coherence tomography angiography) are emerging technologies in this field. Application of AI-DL is expected to detect subclinical retinal changes and discrete retinal features in predicting systemic conditions including CeVD. CONCLUSIONS: There is extensive and increasing evidence that a range of retinal vascular signs and disease are closely linked to CeVD, including subclinical and clinical CeVD. New technology including AI-DL will allow further translation to clinical utilization.

Reliability of foveal avascular zone metrics automatically measured by Cirrus optical coherence tomography angiography in healthy subjects.

PURPOSE: To investigate the reliability of the foveal avascular zone (FAZ) metrics automatically measured using Cirrus optical coherence tomography angiography (OCTA) embedded algorithm compared to human manual measurement. METHODS: Thirty-five eyes of 35 healthy subjects were enrolled and scanned four times continuously on Zeiss Cirrus HD-OCT 5000. The FAZ metrics (area, circularity and perimeter) of the superficial capillary plexus were measured automatically using the embedded tool and manually measured by the two independent observers using ImageJ. The repeatability of the four scans within all methods of measurements was calculated. The agreement of the manual vs automated measurement was also analyzed. RESULTS: The repeatability of the automated algorithm was only poor to moderate (intraclass correlation coefficients [ICCs] for the area, perimeter and circularity were 0.600, 0.405 and 0.221, respectively) while the repeatability of the manually measured FAZ area and perimeter was good [([ICCs] ranged from 0.845 to 0.877) except the circularity (ICC = 0.538 to 0.608)]. The ranges of 95% limits of agreement between the manual measurements by the two observers were only 20% to 31% of those of automated-manual agreement. The Cirrus inbuilt algorithm obviously outlined the border of FAZ wrongly in 22.9% cases. CONCLUSION: Caution should be taken when using the automated measurement results of FAZ metrics in Cirrus OCTA, because of the low repeatability and poor agreement compared with the manual measurement.

Potential retinal biomarkers for dementia: what is new?

PURPOSE OF REVIEW: To summarize the current findings on clinical retinal diseases and retinal imaging changes with dementia, focusing on Alzheimer's disease. RECENT FINDINGS: Studies observed that clinical retinal diseases such as age-related macular degeneration, open-angle glaucoma and diabetic retinopathy are related to dementia, but the associations are not entirely consistent. In terms of the retinal neuronal structure, multiple retinal neuronal layers are significantly thinner in Alzheimer's disease dementia, such as the parapapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GC-IPL). Recent studies further demonstrated that macular GC-IPL and macular RNFL are also significantly thinner in the preclinical stage of Alzheimer's disease. A thinner RNFL is also associated with a significantly increased risk of developing both cognitive decline and Alzheimer's disease dementia. In addition, studies consistently showed that retinal vascular changes are associated with poorer cognitive performance, as well as prevalent and incident Alzheimer's disease dementia. SUMMARY: The current findings support the concept that changes in the retina, particular in retinal neuronal structure and vasculature, can reflect the status of cerebral neuronal structure and vasculature, highlighting the potential role of retinal changes as biomarkers of dementia.

Retinal Nerve Fiber Layer Thickness in a Multiethnic Normal Asian Population: The Singapore Epidemiology of Eye Diseases Study.

PURPOSE: To describe variations in retinal nerve fiber layer (RNFL) thickness based on spectral-domain (SD) OCT in a multiethnic Asian population. DESIGN: Population-based, cross-sectional study. PARTICIPANTS: Ethnic Chinese, Malay, and Indian adults older than 48 years without glaucoma who were recruited from the Singapore Epidemiology of Eye Diseases Study. METHODS: All participants underwent standardized systemic and ocular examinations. Retinal nerve fiber layer thickness was measured using SD OCT. Participants with poor-quality scans were excluded. Linear regression models were used to investigate the associations of ocular and systemic factors with average RNFL thickness. Generalized estimating equation models were used to account for correlation between both eyes. MAIN OUTCOME MEASURE: Average RNFL thickness. RESULTS: Four thousand four hundred seventy-five participants (8178 eyes) consisting of 1371 Chinese, 1303 Malay, and 1801 Indian adults contributed to this analysis. Average RNFL thickness measured was 95.7±9.6 µm in Chinese participants, 94.9±10.6 µm in Malay participants, and 87.3±10.6 µm in Indian participants (P < 0.001). Multivariate analysis adjusted for age, gender, and ethnicity revealed a reduction in RNFL thickness with increased intraocular pressure and axial length (P < 0.001 for both), as well as a diagnosis of diabetes (P = 0.04); greater RNFL thickness was associated with increased disc area (P < 0.001), signal strength (P < 0.001), and low-density lipoprotein cholesterol (P = 0.02). When these significant variables were taken into account, the average RNFL thickness of Indian participants was significantly thinner compared with Chinese participants (7.45 µm thinner on average [95% confidence interval, 6.75-8.15 µm; P < 0.001]), whereas there was no significant difference in average RNFL thickness between Malay and Chinese participants (P = 0.15). CONCLUSIONS: Average and regional RNFL thicknesses were significantly thinner in Indian eyes compared with Chinese and Malay eyes. Results of the study highlight the need to acquire more refined normative data for the comparison of individual patients with others of similar ethnic background while accounting for ocular factors that could influence RNFL thickness. This in turn may improve the sensitivity and specificity of glaucoma detection.

OCT Angiography Metrics Predict Progression of Diabetic Retinopathy and Development of Diabetic Macular Edema: A Prospective Study.

PURPOSE: To prospectively determine the relationship of OCT angiography (OCTA) metrics to diabetic retinopathy (DR) progression and development of diabetic macular edema (DME). DESIGN: Prospective, observational study. PARTICIPANTS: A total of 205 eyes from 129 patients with diabetes mellitus followed up for at least 2 years. METHODS: All participants underwent OCTA with a swept-source OCT device (DRI-OCT Triton, Topcon, Inc, Tokyo, Japan). Individual OCTA images of superficial capillary plexus (SCP) and deep capillary plexus (DCP) were generated by IMAGEnet6 (Basic License 10). After a quality check, automated measurements of foveal avascular zone (FAZ) area, FAZ circularity, vessel density (VD), and fractal dimension (FD) of both SCP and DCP were then obtained. MAIN OUTCOME MEASURES: Progression of DR and development of DME. RESULTS: Over a median follow-up of 27.14 months (interquartile range, 24.16-30.41 months), 28 of the 205 eyes (13.66%) developed DR progression. Of the 194 eyes without DME at baseline, 17 (8.76%) developed DME. Larger FAZ area (hazard ratio [HR], 1.829 per SD increase; 95% confidence interval [CI], 1.332-2.512), lower VD (HR, 1.908 per SD decrease; 95% CI, 1.303-2.793), and lower FD (HR, 4.464 per SD decrease; 95% CI, 1.337-14.903) of DCP were significantly associated with DR progression after adjusting for established risk factors (DR severity, glycated hemoglobin, duration of diabetes, age, and mean arterial blood pressure at baseline). Lower VD of SCP (HR, 1.789 per SD decrease; 95% CI, 1.027-4.512) was associated with DME development. Compared with the model with established risk factors alone, the addition of OCTA metrics improved the predictive discrimination of DR progression (FAZ area of DCP, C-statistics 0.723 vs. 0.677, P < 0.001; VD of DCP, C-statistics 0.727 vs. 0.677, P = 0.001; FD of DCP, C-statistics 0.738 vs. 0.677, P < 0.001) and DME development (VD of SCP, C-statistics 0.904 vs. 0.875, P = 0.036). CONCLUSIONS: The FAZ area, VD, and FD of DCP predict DR progression, whereas VD of SCP predicts DME development. Our findings provide evidence to support that OCTA metrics improve the evaluation of risk of DR progression and DME development beyond traditional risk factors.