Retinal neural dysfunction in diabetes revealed with handheld chromatic pupillometry.

BACKGROUND: To evaluate the ability of handheld chromatic pupillometry to reveal and localise retinal neural dysfunction in diabetic patients with and without diabetic retinopathy (DR). METHODS: This cross-sectional study included 82 diabetics (DM) and 93 controls (60.4 ± 8.4 years, 44.1% males). DM patients included those without (n = 25, 64.7 ± 6.3 years, 44.0% males) and with DR (n = 57, 60.3 ± 8.5 years, 64.9% males). Changes in horizontal pupil radius in response to blue (469 nm) and red (640 nm) light stimuli were assessed monocularly, in clinics, using a custom-built handheld pupillometer. Pupillometric parameters (phasic constriction amplitudes [predominantly from the outer retina], maximal constriction amplitudes [from the inner and outer retina] and post-illumination pupillary responses [PIPRs; predominantly from the inner retina]) were extracted from baseline-adjusted pupillary light response traces and compared between controls, DM without DR, and DR. Net PIPR was defined as the difference between blue and red PIPRs. RESULTS: Phasic constriction amplitudes to blue and red lights were decreased in DR compared to controls (p < 0.001; p < 0.001). Maximal constriction amplitudes to blue and red lights were decreased in DR compared to DM without DR (p < 0.001; p = 0.02), and in DM without DR compared to controls (p < 0.001; p = 0.005). Net PIPR was decreased in both DR and DM without DR compared to controls (p = 0.02; p = 0.03), suggesting a wavelength-dependent (and hence retinal) pupillometric dysfunction in diabetic patients with or without DR. CONCLUSIONS: Handheld chromatic pupillometry can reveal retinal neural dysfunction in diabetes, even without DR. Patients with DM but no DR displayed primarily inner retinal dysfunction, while patients with DR showed both inner and outer retinal dysfunction.

Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.

Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures: Use of a deep learning system. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results: In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.

SCLERAL BUCKLING VERSUS VITRECTOMY IN THE MANAGEMENT OF MACULA-OFF PRIMARY RHEGMATOGENOUS RETINAL DETACHMENT: A COMPARISON OF VISUAL OUTCOMES.

PURPOSE: To compare visual outcomes between pars plana vitrectomy (PPV) with or without scleral buckling (SB) and SB alone in the management of uncomplicated macula-off primary rhegmatogenous retinal detachment. METHODS: Case-control study of 723 patients with uncomplicated macula-off primary rhegmatogenous retinal detachment seen at the Singapore National Eye Centre from 2005 to 2011. The primary outcome measure was the proportion of eyes achieving functional success, defined as logMAR best-corrected visual acuity of ≤0.3 logMAR at 6 months postoperatively. Multivariable logistic regression analysis was performed adjusting for the following preoperative covariates: age, gender, race, lens status, number of tears found, presence of proliferative vitreoretinopathy, operative procedure, logMAR best-corrected visual acuity, and duration of symptoms. RESULTS: Three hundred and eight eyes underwent SB alone, and 415 eyes underwent PPV ± SB. In the SB group, 133 eyes (43.2%) achieved functional success compared with 116 eyes (28.0%) in the PPV ± SB group. This difference was statistically significant on both univariate (P < 0.001) and multivariable analyses (OR: 1.51, 95% CI: 1.03-2.21, P = 0.03). CONCLUSION: Scleral buckling alone may achieve visual outcomes that are at least comparable with PPV ± SB in the management of macula-off primary rhegmatogenous retinal detachment.

Trends and factors related to outcomes for primary rhegmatogenous retinal detachment surgery in a large asian tertiary eye center.

PURPOSE: To describe trends and outcomes of vitreoretinal surgery for primary rhegmatogenous retinal detachment in a large Asian tertiary eye center. METHODS: Retrospective review of 1,530 eyes with primary retinal detachment between 2005 and 2011 managed at the Singapore National Eye Center by one of the following: scleral buckling (SB), pars plana vitrectomy (PPV), and combined SB and PPV (SB + PPV). Anatomical and functional outcomes were assessed. RESULTS: There was a trend toward PPV and PPV + SB as the primary reattachment procedure from 2005 to 2011. The primary anatomical success rate for PPV (78.6%) was worse than that for SB (88.8%) or SB + PPV (89.0%, P = 0.000). Final anatomical success rates were similar for all 3 procedures: SB 97.7%, PPV 95.2%, and SB + PPV 96.4%. Better functional success was achieved in the SB group (86.1%) than both the PPV (72.5%) and SB + PPV groups (77.5%, P = 0.000), partly attributable to the less complex nature of retinal detachments in the SB group. Older age and proliferative vitreoretinopathy were related to the poor functional outcomes in both phakic and pseudophakic eyes. CONCLUSION: There was an increasing trend toward PPV and PPV + SB as the primary retinal reattachment surgery from 2005 through to 2011. High rates of anatomical and functional outcomes were achieved with SB, PPV, and SB + PPV, proliferative vitreoretinopathy and older age were negatively correlated with the functional success in both phakic and pseudophakic eyes.

International incidence and temporal trends for rhegmatogenous retinal detachment: A systematic review and meta-analysis.

We set out to estimate the international incidence of rhegmatogenous retinal detachment (RRD) and to evaluate its temporal trend over time. There is a lack of robust estimates on the worldwide incidence and trend for RRD, a major cause of acute vision loss. We conducted a systematic review of RRD incidence. The electronic databases PubMed, Scopus, and Thomson Reuters' Web of Science were searched from inception through 2nd June 2022. Random-effects meta-analysis model with logit transformation was performed to obtain pooled annual incidence estimates of RRD. Pooled analysis was performed to evaluate the temporal trend of RRD incidence of the 20,958 records identified from the database searches; 33 studies from 21 countries were included for analysis (274,836 cases of RRD in 273,977 persons). Three of the 6 global regions as defined by WHO had studies that met the inclusion and exclusion criteria of the study. The annual international incidence of RRD was estimated to be 12.17 (95% confidence interval [CI] 10.51-14.09) per 100,000 population; with an increasing temporal trend of RRD at 5.4 per 100,000 per decade (p 0.001) from 1997 to 2019. Amongst world regions, the RRD incidence was highest in Europe (14.52 [95% CI 11.79 - 17.88] per 100,000 population), followed by Western Pacific (10.55 [95% CI 8.71-12.75] per 100,000 population) and Regions of Americas (8.95 [95% CI 6.73-11.92] per 100,000 population). About one in 10,000 persons develop RRD each year. There is evidence of increasing trend for RRD incidence over time, with possibly doubling of the current incidence rate within the next 2 decades.

Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs.

BACKGROUND: Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular risk stratification system based on deep-learning-predicted CAC from retinal photographs. METHODS: We used 216 152 retinal photographs from five datasets from South Korea, Singapore, and the UK to train and validate the algorithms. First, using one dataset from a South Korean health-screening centre, we trained a deep-learning algorithm to predict the probability of the presence of CAC (ie, deep-learning retinal CAC score, RetiCAC). We stratified RetiCAC scores into tertiles and used Cox proportional hazards models to evaluate the ability of RetiCAC to predict cardiovascular events based on external test sets from South Korea, Singapore, and the UK Biobank. We evaluated the incremental values of RetiCAC when added to the Pooled Cohort Equation (PCE) for participants in the UK Biobank. FINDINGS: RetiCAC outperformed all single clinical parameter models in predicting the presence of CAC (area under the receiver operating characteristic curve of 0·742, 95% CI 0·732-0·753). Among the 527 participants in the South Korean clinical cohort, 33 (6·3%) had cardiovascular events during the 5-year follow-up. When compared with the current CAC risk stratification (0, >0-100, and >100), the three-strata RetiCAC showed comparable prognostic performance with a concordance index of 0·71. In the Singapore population-based cohort (n=8551), 310 (3·6%) participants had fatal cardiovascular events over 10 years, and the three-strata RetiCAC was significantly associated with increased risk of fatal cardiovascular events (hazard ratio [HR] trend 1·33, 95% CI 1·04-1·71). In the UK Biobank (n=47 679), 337 (0·7%) participants had fatal cardiovascular events over 10 years. When added to the PCE, the three-strata RetiCAC improved cardiovascular risk stratification in the intermediate-risk group (HR trend 1·28, 95% CI 1·07-1·54) and borderline-risk group (1·62, 1·04-2·54), and the continuous net reclassification index was 0·261 (95% CI 0·124-0·364). INTERPRETATION: A deep learning and retinal photograph-derived CAC score is comparable to CT scan-measured CAC in predicting cardiovascular events, and improves on current risk stratification approaches for cardiovascular disease events. These data suggest retinal photograph-based deep learning has the potential to be used as an alternative measure of CAC, especially in low-resource settings. FUNDING: Yonsei University College of Medicine; Ministry of Health and Welfare, Korea Institute for Advancement of Technology, South Korea; Agency for Science, Technology, and Research; and National Medical Research Council, Singapore.

Retinal photograph-based deep learning algorithms for myopia and a blockchain platform to facilitate artificial intelligence medical research: a retrospective multicohort study.

BACKGROUND: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability. METHODS: In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China. FINDINGS: The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959-0·977) or higher for myopic macular degeneration and 0·913 (0·906-0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957-0·994] for myopic macular degeneration and 0·973 [0·941-0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries. INTERPRETATION: Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine. FUNDING: None.

Intravitreal Injection with a Conjunctival Injection Device: A Single-Center Experience.

Purpose: To evaluate the clinical performance of the intravitreal injection assistant device (InVitria) compared with the conventional freehand technique for delivering intravitreal injections. Methods: Seventy patients were randomized to receive intravitreal injections via the conventional freehand technique while 70 received injections using the InVitria. Half of all procedures in each group were performed by junior surgeons, while the rest were performed by senior surgeons. Results: Mean injections times were 90.0 ± 23.3 seconds and 64.9 ± 26.8 seconds for conventional versus InVitria (P < 0.001). Mean injection times with the conventional technique were 85.5 ± 23.0 seconds vs. 94.2 ± 23.0 seconds for senior versus junior surgeons (P = 0.120). Mean injection times with the InVitria were 56.1 ± 26.1 seconds vs. 66.3 ± 26.9 seconds (P = 0.211) for senior versus junior surgeons. There were no significant differences in pain scores regardless of technique (conventional versus In Vitria: 2.03 ± 1.73 vs. 2.13 ± 2.20, P = 0.770). Conclusions: In our experience, the InVitria is a comparable alternative to the conventional freehand technique of delivering intravitreal injections, with the potential for faster injection times and without compromising on patient comfort. Translational Relevance: The study provides evidence to suggest that the InVitria may be deployed effectively in clinical practice.

Choroidal Remodeling in Age-related Macular Degeneration and Polypoidal Choroidal Vasculopathy: A 12-month Prospective Study.

Choroid thinning occurs in age-related macular degeneration (AMD). However, it remains unclear whether the reduction is due to reduction in choroidal vessels or shrinkage of choroidal stroma, or both. The purpose of this study was to evaluate the changes of the choroidal vascular and stromal area in 118 patients with typical AMD (t-AMD) and polypoidal choroidal vasculopathy (PCV) over a 12-month period. We used spectral-domain optical coherence tomography (SD-OCT) with enhanced depth imaging (EDI) mode to measure the subfoveal choroidal thickness (CT), central retinal thickness (CRT) and choroidal vascularity index (CVI - ratio of luminal area to total choroidal area). At baseline, PCV eyes had higher CRT (471.6 µm vs 439.1 µm, p = 0.02), but comparable subfoveal CT and CVI, compared to t-AMD. Eyes with high CVI at baseline showed marked reduction in stromal area compared with eyes with average or low CVI. Over 12 months, CRT and subfoveal CT significantly decreased (p < 0.001) in both subtypes. Eyes with high baseline CVI showed significant CVI reduction from baseline to month 12 (p < 0.001), whereas eyes with average to low baseline CVI showed increase in CVI. These differences in choroidal vascularity may reflect different predominant pathogenic processes and remodeling in AMD eyes with varying spectrum.

A prospective study of treatment patterns and 1-year outcome of Asian age-related macular degeneration and polypoidal choroidal vasculopathy.

OBJECTIVE: To study the treatment patterns and visual outcome over one year in Asian patients with choroidal neovascular membrane secondary to age-related macular degeneration (AMD-CNV) and polypoidal choroidal vasculopathy (PCV). DESIGN: Prospective cohort, non-interventional study. METHODS: 132 treatment-naïve patients who received treatment for AMD-CNV and PCV were included. All patients underwent standardized examination procedures including retinal imaging at baseline and follow-up. AMD-CNV and PCV were defined on fundus fluorescein angiography and indocyanine green angiography at baseline. Patients were treated according to standard of care.We report the visual acuity (VA) and optical coherence tomography (OCT) measurements at baseline, month 3 and month 12 The factors influencing month 12 outcomes were analyzed. MAIN OUTCOME MEASURE: Type of treatment, number of Anti-vascular endothelial growth factor (VEGF) treatments, visual outcome over one year. RESULTS: Anti-VEGF monotherapy was the initial treatment in 89.1% of AMD-CNV, but only 15.1% of PCV. The mean number of anti-VEGF injections up to month 12 was 3.97 (4.51 AMD-CNV, 3.43 PCV, p = 0.021). Baseline OCT, month 3 OCT and month 3 VA were significant in determining continuation of treatment after month 3. At month 12, mean VA improved from 0.82 (∼20/132) at baseline to 0.68 (∼20/96) at month 12 (mean gain 6.5 ETDRS letters, p = 0.002). 34.2% of eyes (38/113 eyes) gained ≥15 ETDRS letters and 14.4% (16/113 eyes) lost ≥15 ETDRS letters. There were no significant differences in visual outcome between AMD-CNV and PCV (p = 0.51). Factors predictive of month 12 visual outcome were baseline VA, baseline OCT central macular thickness, month 3 VA and age. CONCLUSIONS: There is significant variation in treatment patterns in Asian eyes with exudative maculopathy. There is significant visual improvement in all treatment groups at one year. These data highlight the need for high quality clinical trial data to provide evidence-based management of Asian AMD.