Nonmydriatic Fundus Photography Research Articles

Association between dietary consumption of fatty acids and age-related macular degeneration in the National Health and Nutrition Examination Survey

The aim of this study is to assess the relationship between dietary intake of fatty acids and the age-related macular degeneration (AMD) in the United States population. Adult participants of the 2005–2008 National Health and Nutrition Examination Survey (NHANES) were included in this nationwide cross-sectional study. Dietary fatty acid intake was obtained from two 24-h dietary recall interviews. The intake of dietary fatty acids was analyzed as a continuous and categorical variable. AMD status was assessed using nonmydriatic fundus photographs. Univariate and multivariate logistic regression analyses were used to assess the association between dietary fatty acid intake and AMD. The unweighted population included 4702 individuals of whom 374 had AMD. After adjusting for relevant variables, each 1 unit increase (1 mg/1000 kcal) intake of EPA (OR: 0.996, 95% CI: 0.993–0.996, P = 0.018), DPA (OR: 0.976, 95% CI: 0.962–0.990, P = 0.002), and DHA (OR: 0.996, 95% CI: 0.994–0.999, P = 0.003) were significantly decreased odds of any AMD. The highest versus lowest quartile of EPA (OR: 0.476, P for trend < 0.001), DPA (OR: 0.467, P for trend = 0.005) and DHA (OR: 0.586, P for trend = 0.008) were negatively associated with the odds of any AMD. Subgroup analysis showed that higher quartiles of EPA (OR: 0.461, P for trend < 0.002), DPA (OR: 0.467, P for trend = 0.006) and DHA (OR: 0.578, P for trend = 0.007) exhibited a negative association with early AMD. The study found no significant association between the intake of dietary fatty acids, including n-3 PUFA, and the odds of late AMD. In the 2005–2008 NHANES population, higher dietary DHA, DPA and EPA intake associated with decreased odds of early AMD. However, no clear association was found between specific types of FAs and late AMD.

Risk Factors of Age-Related Macular Degeneration in a Population-Based Study: Results from SHIP-TREND-1 (Study of Health in Pomerania-TREND-1).

BACKGROUND Age-related macular degeneration (AMD) is the most common cause of visual impairment in the elderly population in industrialized countries. The Study of Health in Pomerania (SHIP) with its cohort SHIP-TREND was designed to investigate risk factors and clinical disorders in the general population of northeast Germany. This work focused on the first follow-up of SHIP-TREND and determined associated modifiable risk factors of AMD. Modifying risk factors is important to slow the progression of early AMD as there is currently no treatment for the late stage of geographic atrophy. Understanding AMD-associated risk factors also plays an important role in the development of therapeutic concepts. MATERIAL AND METHODS Between 2016 and 2019, data were collected from a total of 2507 initially randomly selected subjects from the general population aged 28 to 89 years. Non-mydriatic fundus photography of the right eye was performed in 2489 subjects. Grading of AMD was performed using the Rotterdam classification system. RESULTS We included 1418 gradable fundus photographs in the analysis. The risk of AMD changes increased with age and was positively correlated with HDL cholesterol, fT3, and low educational level. In men, BMI and cigarette smoking were also positively associated with AMD changes. CONCLUSIONS This study emphasizes the consideration of various metabolic pathways for the development of therapeutic concepts.

Application of non-mydriatic fundus photography-assisted telemedicine in diabetic retinopathy screening.

Early screening and accurate staging of diabetic retinopathy (DR) can reduce blindness risk in type 2 diabetes patients. DR's complex pathogenesis involves many factors, making ophthalmologist screening alone insufficient for prevention and treatment. Often, endocrinologists are the first to see diabetic patients and thus should screen for retinopathy for early intervention. To explore the efficacy of non-mydriatic fundus photography (NMFP)-enhanced telemedicine in assessing DR and its various stages. This retrospective study incorporated findings from an analysis of 93 diabetic patients, examining both NMFP-assisted telemedicine and fundus fluorescein angiography (FFA). It focused on assessing the concordance in DR detection between these two methodologies. Additionally, receiver operating characteristic (ROC) curves were generated to determine the optimal sensitivity and specificity of NMFP-assisted telemedicine, using FFA outcomes as the standard benchmark. In the context of DR diagnosis and staging, the kappa coefficients for NMFP-assisted telemedicine and FFA were recorded at 0.775 and 0.689 respectively, indicating substantial intermethod agreement. Moreover, the NMFP-assisted telemedicine's predictive accuracy for positive FFA outcomes, as denoted by the area under the ROC curve, was remarkably high at 0.955, within a confidence interval of 0.914 to 0.995 and a statistically significant P-value of less than 0.001. This predictive model exhibited a specificity of 100%, a sensitivity of 90.9%, and a Youden index of 0.909. NMFP-assisted telemedicine represents a pragmatic, objective, and precise modality for fundus examination, particularly applicable in the context of endocrinology inpatient care and primary healthcare settings for diabetic patients. Its implementation in these scenarios is of paramount significance, enhancing the clinical accuracy in the diagnosis and therapeutic management of DR. This methodology not only streamlines patient evaluation but also contributes substantially to the optimization of clinical outcomes in DR management.

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

Non-mydriatic fundus photography (NMFP) plays a vital role in diagnosing eye diseases, with its performance primarily dependent on the autofocus process. However, even minor maloperations or eye micro-movements can compromise fundus imaging quality, leading to autofocus inaccuracy and a heightened risk of misdiagnosis. To enhance the autofocus performance in NMFP, a fast and robust fundus autofocus method with adaptive window and path-optimized search is proposed. In this method, the adaptive focus window is used to suppress irrelevant image contents and correct the sharpness curve, and the path-optimized search is constructed to overcome the curve’s local extrema, in order to achieve rapid focus position convergence. This method was simulated and clinically studied with the self-developed autofocus system for NMFP. The results of 80 cases of human eye imaging show that, compared with similar autofocus methods, this method achieves a focus success rate of 90% with the least axial scanning, and can adapt to non-ideal imaging conditions such as pupil misalignment, eyelash occlusion, and nystagmus.

Quality assessment of non-mydriatic fundus photographs for glaucoma screening in primary healthcare centres: a real-world study

BackgroundThis study assessed the quality distribution of non-mydriatic fundus photographs (NMFPs) in real-world glaucoma screening and analysed its influencing factors.MethodsThis cross-sectional study was conducted in primary healthcare centres in the...

Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department

The Fundus photography vs Ophthalmoscopy Trial Outcomes in the Emergency Department (FOTO-ED) studies showed that ED providers poorly recognized funduscopic findings in patients in the ED. We tested a modified version of the Brain and Optic Nerve Study Artificial Intelligence (BONSAI) deep learning system on nonmydriatic fundus photographs from the FOTO-ED studies to determine if the deep learning system could have improved the detection of papilledema had it been available to ED providers as a real-time diagnostic aid. Retrospective secondary analysis of a cohort of patients included in the FOTO-ED studies. The testing data set included 1608 photographs obtained from 828 patients in the FOTO-ED studies. Photographs were reclassified according to the optic disc classification system used by the deep learning system ("normal optic discs," "papilledema," and "other optic disc abnormalities"). The system's performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 1-vs-rest strategy, with reference to expert neuro-ophthalmologists. The BONSAI deep learning system successfully distinguished normal from abnormal optic discs (AUC 0.92 [95% confidence interval {CI} 0.90-0.93]; sensitivity 75.6% [73.7%-77.5%] and specificity 89.6% [86.3%-92.8%]), and papilledema from normal and others (AUC 0.97 [0.95-0.99]; sensitivity 84.0% [75.0%-92.6%] and specificity 98.9% [98.5%-99.4%]). Six patients with missed papilledema in 1 eye were correctly identified by the deep learning system as having papilledema in the other eye. The BONSAI deep learning system was able to reliably identify papilledema and normal optic discs on nonmydriatic photographs obtained in the FOTO-ED studies. Our deep learning system has excellent potential as a diagnostic aid in EDs and non-ophthalmology clinics equipped with nonmydriatic fundus cameras. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Diabetic Retinopathy Screening at the Point of Care (DR SPOC): detecting undiagnosed and vision-threatening retinopathy by integrating portable technologies within existing services

IntroductionThe aim of this study was to determine the prevalence of diabetic retinopathy (DR) in a low socioeconomic region of a high-income country, as well as determine the diagnostic utility...

Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study.

To investigate the prevalence of fundus tessellation (FT), and the threshold for screening FT using an artificial intelligence (AI) technology in Chinese children. The Nanjing Eye Study was a population-based cohort study conducted in children born between September 2011 and August 2012 in Yuhuatai District of Nanjing. The data presented in this paper were obtained in 2019, when these children were 7years old and underwent 45° non-mydriatic fundus photography. FT in whole fundus, macular area, and peripapillary area was manually recognized from fundus photographs and classified into three grades. Fundus tessellation density (FTD) in these areas was obtained by calculating the average exposed choroid area per unit area using artificial intelligence (AI) technology based on fundus photographs. The threshold for screening FT using FTD was determined using receiver operating characteristic (ROC) curve analysis. Among 1062 enrolled children (mean [± standard deviation] spherical equivalent: - 0.28 ± 0.70 D), the prevalence of FT was 42.18% in the whole fundus (grade 1: 36.53%; grade 2: 5.08%; grade 3: 0.56%), 45.57% in macular area (grade 1: 43.5%; grade 2: 1.60%; grade 3: 0.50%), and 49.72% in peripapillary area (grade 1: 44.44%; grade 2: 4.43%; grade 3: 0.85%), respectively. The threshold value of FTD for screening severe FT (grade ≥ 2) was 0.049 (area under curve [AUC] 0.985; sensitivity 98.3%; specificity 92.3%) in the whole fundus, 0.069 (AUC 0.987; sensitivity 95.5%; specificity 96.2%) in the macular area, and 0.094 (AUC 0.980; sensitivity 94.6%; specificity 94.2%) in the peripapillary area, respectively. Fundus tessellation affected approximately 40 in 100 children aged 7years in China, indicating the importance and necessity of early FT screening. The threshold values of FTD provided by this study had high accuracy for detecting severe FT and might be applied for rapid screening.

Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China

The current epidemic status of diabetic retinopathy in China is unclear. A national prevalence survey of diabetic complications was conducted. 50,564 participants with gradable non-mydriatic fundus photographs were enrolled. The prevalence rates (95% confidence intervals) of diabetic retinopathy and vision-threatening diabetic retinopathy were 16.3% (15.3%–17.2%) and 3.2% (2.9%–3.5%), significantly higher in the northern than in the southern regions. The differences in prevalence between those who had not attained a given metabolic goal and those who had were more pronounced for Hemoglobin A1c than for blood pressure and low-density lipoprotein cholesterol. The participants with vision-threatening diabetic retinopathy had significantly higher proportions of visual impairment and blindness than those with non-vision-threatening diabetic retinopathy. The likelihoods of diabetic retinopathy and vision-threatening diabetic retinopathy were also associated with education levels, household income, and multiple dietary intakes. Here, we show multi-level factors associated with the presence and the severity of diabetic retinopathy.

Fundus Tessellated Density Assessed by Deep Learning in Primary School Children.

To explore associations of fundus tessellated density (FTD) and compare characteristics of different fundus tessellation (FT) distribution patterns, based on artificial intelligence technology using deep learning. Comprehensive ocular examinations were conducted in 577 children aged 7 years old from a population-based cross-sectional study, including biometric measurement, refraction, optical coherence tomography angiography, and 45° nonmydriatic fundus photography. FTD was defined as the average exposed choroid area per unit area of the fundus, and obtained by artificial intelligence technology. The distribution of FT was classified into the macular pattern and the peripapillary pattern according to FTD. The mean FTD was 0.024 ± 0.026 in whole fundus. Multivariate regression analysis showed that greater FTD was significantly correlated with thinner subfoveal choroidal thickness, larger parapapillary atrophy, greater vessel density inside the optic disc, larger vertical diameter of optic disc, thinner retinal nerve fiber layer, and longer distance from optic disc center to macular fovea (all P < 0.05). The peripapillary distributed group had larger parapapillary atrophy (0.052 ± 0.119 vs 0.031 ± 0.072), greater FTD (0.029 ± 0.028 vs 0.015 ± 0.018), thinner subfoveal choroidal thickness (297.66 ± 60.61 vs 315.33 ± 66.46), and thinner retinal thickness (285.55 ± 10.89 vs 288.03 ± 10.31) than the macular distributed group (all P < 0.05). FTD can be applied as a quantitative biomarker to estimate subfoveal choroidal thickness in children. The role of blood flow inside optic disc in FT progression needs further investigation. The distribution of FT and the peripapillary pattern correlated more with myopia-related fundus changes than the macular pattern. Artificial intelligence can evaluate FT quantitatively in children, and has potential value for assisting in myopia prevention and control.

Association of autoantibody levels with different stages of age-related macular degeneration (AMD): Results from the population-based Gutenberg Health Study (GHS)

PurposeAnti-retinal autoantibodies are assumed to be associated with age-related macular degeneration (AMD). To our knowledge, this is the first evaluation of autoantibodies in human sera of participants with different stages of AMD in a large population-based, observational cohort study in Germany.MethodsThe Gutenberg Health Study (GHS) is a population-based, observational cohort study in Germany, including 15,010 participants aged between 35 and 74. Amongst others, non-mydriatic fundus photography (Visucam PRO NM™, Carl Zeiss Meditec AG, Jena, Germany) was performed. Fundus images of the first 5000 participants were graded based on the Rotterdam Eye Study classification. Sera of participants with AMD (n=541) and sera of age-matched participants without AMD (n=490) were analyzed by antigen-microarrays. Besides descriptive statistics, autoantibody-levels were compared by Mann-Whitney-U test and the associations of level of autoantibodies with AMD were calculated by logistic regression analysis. Likewise, possible associations of the autoantibodies and both clinical and laboratory parameters on AMD subjects were analyzed.ResultsAutoantibodies against transferrin (p<0.001) were significantly downregulated in participants with early AMD and soft, distinct drusen (≥63 μm) or pigmentary abnormalities only compared to Controls. Mitogen-activated protein kinase 3 (p=0.041), glutathione peroxidase 4 (p=0.048), clusterin (p=0.045), lysozyme (p=0.19), protein kinase C substrate 80K-H (p=0.02), heat shock 70 kDa protein 1A (p=0.04) and insulin (p=0.018) show a trend between Control and participants with early AMD and soft, distinct drusen (≥63 μm) or pigmentary abnormalities only.ConclusionsThis study contributes to a growing knowledge of autoantibodies in association with different AMD stages compared to controls in the context of a large population-based study in Germany. Especially autoantibodies against inflammatory proteins were downregulated in participants with early AMD and soft, distinct drusen (≥63 μm) or pigmentary abnormalities only.

Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting

AimDiabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.MethodsIt was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.ResultsThe agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891–0.979).ConclusionsThe Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns.

OPTIMAL TRANSPORT GUIDED UNSUPERVISED LEARNING FOR ENHANCING LOW-QUALITY RETINAL IMAGES.

Real-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections, and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result in inaccuracy or ambiguity in clinical diagnoses. In this paper, we proposed a simple but effective end-to-end framework for enhancing poor-quality retinal fundus images. Leveraging the optimal transport theory, we proposed an unpaired image-to-image translation scheme for transporting low-quality images to their high-quality counterparts. We theoretically proved that a Generative Adversarial Networks (GAN) model with a generator and discriminator is sufficient for this task. Furthermore, to mitigate the inconsistency of information between the low-quality images and their enhancements, an information consistency mechanism was proposed to maximally maintain structural consistency (optical discs, blood vessels, lesions) between the source and enhanced domains. Extensive experiments were conducted on the EyeQ dataset to demonstrate the superiority of our proposed method perceptually and quantitatively.

The average 30-minute post-prandial C-peptide predicted diabetic retinopathy progress: a retro-prospective study

BackgroundThe conclusion between Connecting peptide (C-peptide) and diabetic chronic complication was controversial. The purpose of this study is to explore the possible association between average C-peptide with diabetic retinopathy (DR) progression in Chinese patients with type 2 diabetes.MethodsThis is a retro-prospective study. 622 patients with type 2 diabetes were included. DR was evaluated using non-mydriatic fundus photography and DR progression was defined as any deterioration of either eye. Fasting and postprandial c-peptide levels were assayed at baseline and follow-up period. Differences between continuous variables were compared using the Mann–Whitney U test; and categorical variables were analyzed by the chi-square test. Correlation between parameters and 30-minute postprandial C-peptide were determined by Spearman correlation test. The relationship between C-peptide and DR progression was evaluated by multivariable binary logistic regression. Two-tailed P-values < 0.05 were regarded as statistically significant.ResultsDR was present in 162 (26.0%) patients at baseline, and 26.4% of patients were found progression of DR at follow-up. Patients with progression of DR had lower average levels of 30-minute postprandial C-peptide (2.01 ng/ml vs. 2.6 ng/ml, p = 0.015) and 120-minute postprandial C-peptide (3.17 ng/ml vs. 3.92 ng/ml, p < 0.029), as well as average increment of 30-minute (0.41 ng/ml vs. 0.64 ng/ml, p = 0.015) and 120-minute postprandial C-peptide (1.48 ng/ml vs. 1.93 ng/ml, p < 0.017), than those without DR aggravation. Multivariate logistic regression analysis determined that 30-minute postprandial C-peptide and its increment were related to reduced odds ratios for DR progression (odds ratios [OR] = 0.83 and 0.74, respectively).ConclusionOur results suggest that the Average 30-minute post-prandial C-peptide and increment were negatively correlated with DR progression, which further demonstrates the importance to preserve β-cell residual function in the prevention for DR progression.Trial registrationNot applicable.

Incidence and Progression of Diabetic Retinopathy in American Indian and Alaska Native Individuals Served by the Indian Health Service, 2015-2019

Estimates of diabetic retinopathy (DR) incidence and progression in American Indian and Alaska Native individuals are based on data from before 1992 and may not be informative for strategizing resources and practice patterns. To examine incidence and progression of DR in American Indian and Alaska Native individuals. This was a retrospective cohort study conducted from January 1, 2015, to December 31, 2019, and included adults with diabetes and no evidence of DR or mild nonproliferative DR (NPDR) in 2015 who were reexamined at least 1 time during the 2016 to 2019 period. The study setting was the Indian Health Service (IHS) teleophthalmology program for diabetic eye disease. Development of new DR or worsening of mild NPDR in American Indian and Alaska Native individuals with diabetes. Outcomes were any increase in DR, 2 or more (2+) step increases, and overall change in DR severity. Patients were evaluated with nonmydriatic ultra-widefield imaging (UWFI) or nonmydriatic fundus photography (NMFP). Standard risk factors were included. The total cohort of 8374 individuals had a mean (SD) age of 53.2 (12.2) years and a mean (SD) hemoglobin A1c level of 8.3% (2.2%) in 2015, and 4775 were female (57.0%). Of patients with no DR in 2015, 18.0% (1280 of 7097) had mild NPDR or worse in 2016 to 2019, and 0.1% (10 of 7097) had PDR. The incidence rate from no DR to any DR was 69.6 cases per 1000 person-years at risk. A total of 6.2% of participants (441 of 7097) progressed from no DR to moderate NPDR or worse (ie, 2+ step increase; 24.0 cases per 1000 person-years at risk). Of patients with mild NPDR in 2015, 27.2% (347 of 1277) progressed to moderate NPDR or worse in 2016 to 2019, and 2.3% (30 of 1277) progressed to severe NPDR or worse (ie, 2+ step progression). Incidence and progression were associated with expected risk factors and evaluation with UWFI. In this cohort study, the estimates of DR incidence and progression were lower than those previously reported for American Indian and Alaska Native individuals. The results suggest extending the time between DR re-evaluations for certain patients in this population, if follow-up compliance and visual acuity outcomes are not jeopardized.

Portable widefield fundus camera with high dynamic range imaging capability.

Fundus photography is indispensable for the clinical detection and management of eye diseases. Low image contrast and small field of view (FOV) are common limitations of conventional fundus photography, making it difficult to detect subtle abnormalities at the early stages of eye diseases. Further improvements in image contrast and FOV coverage are important for early disease detection and reliable treatment assessment. We report here a portable, wide FOV fundus camera with high dynamic range (HDR) imaging capability. Miniaturized indirect ophthalmoscopy illumination was employed to achieve the portable design for nonmydriatic, widefield fundus photography. Orthogonal polarization control was used to eliminate illumination reflectance artifacts. With independent power controls, three fundus images were sequentially acquired and fused to achieve HDR function for local image contrast enhancement. A 101° eye-angle (67° visual-angle) snapshot FOV was achieved for nonmydriatic fundus photography. The effective FOV was readily expanded up to 190° eye-angle (134° visual-angle) with the aid of a fixation target without the need for pharmacologic pupillary dilation. The effectiveness of HDR imaging was validated with both normal healthy and pathologic eyes, compared to a conventional fundus camera.

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing.

Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.

Detecting eyes with high risk of angle closure among apparently normal eyes by anterior segment OCT: a health examination center-based model

BackgroundThe main barriers keeping individuals with high-risk of angle closure from seeking eye-care service are the absence of both disease awareness and convenient and low-cost access to the ocular health care system. Present study described the efficacy of a health examination center-based screening model designed to detect eyes with high risk of angle closure (HRAC) among healthy individuals using anterior segment optical coherence tomography (AS-OCT).MethodsFrom March 1 to April 30, 2017, consecutive individuals aged ≥ 40 years undergoing routine physical examinations at a health examination center were invited to enroll. Presenting visual acuity (PVA), intraocular pressure (IOP) measurement, non-mydriatic fundus photography and AS-OCT were performed by three trained nurses. Participants with PVA < 6/12 in the better-seeing eye, IOP ≥ 24 mmHg, or abnormal fundus photography in either eye were referred to the outpatient clinic, but not included in the analysis. Eyes with HRAC were defined as having trabecular-iris angle < 12 degrees in ≥ 3 quadrants. Configuration of the iris was classified into flat, bowing, bombe, thick peripheral iris and mixed mechanism.ResultsAltogether, 991 participants (77.3%) with readable OCT images (mean age 55.5 ± 9.0 years; 58.4% men) were included. HRAC was diagnosed in 78 eyes (7.9%, 61.3 ± 8.2 years, 41.0% men). The prevalence of HRAC increased with age (p < 0.001) and was much higher among women (11.2%) than men (5.5%) (p = 0.001). The mixed mechanism iris configuration was most common among eyes with HRAC (37/78, 47.4%).ConclusionHRAC is prevalent among asymptomatic Chinese adults undergoing routine health screening. Health examination center-based eye screening with AS-OCT administered by non-specialists may be a good model to screen narrow angles in the population at large.

Valérie Biousse: a leader in neuro-ophthalmology

Valérie Biousse: a leader in neuro-ophthalmology

Diabetic Retinopathy Telemedicine Outcomes With Artificial Intelligence-Based Image Analysis, Reflex Dilation, and Image Overread

To examine real-world telemedicine outcomes of diabetic retinopathy (DR) screening with artificial intelligence (AI)-based image analysis, reflex dilation, and secondary image overread in a primary care setting. Validity and reliability analysis. Single institution review of 1052 consecutive adult patients who received diabetic retinopathy photoscreening in the primary care setting over an 18-month period. Nonmydriatic fundus photographs were acquired and analyzed by the IDx-DR AI-based system. When nonmydriatic images were ungradable, reflex dilation (1% tropicamide) and mydriatic photography were performed for repeat AI-based analysis. Manual overread was performed on all images. Patient demographics, clinical characteristics, and screening outcomes were recorded. A total of 965 of 1052 patients (91.7%) had AI-gradable fundus photographs: 580 had gradable nonmydriatic imaging (55.1%) and 440 of 472 patients with ungradable nonmydriatic photographs had reflex dilation (93.2%). One hundred thirty-eight of 965 patients (14.3%) were AI-graded as "positive" (greater than mild NPDR) and 827 of 965 were "negative" (85.7%), with 100% sensitivity (95% CI 90.8-100%), 89.2% specificity (95% CI 87.0-91.1%), 27.5% positive predictive value (95% CI 24.0-31.4%), and 100% negative predictive value (95% CI 99.6-100%) compared with manual overread assessment of greater than mild NPDR requiring further evaluation with a comprehensive dilated examination. Image gradeability was inversely related to patient age: 93.5% gradable (61.9% nonmydriatic) for patients aged <70 years vs 85.3% (31.0% nonmydriatic) for patients aged 70+ years (P < .001). Incorporation of AI-based image analysis into real-world primary care diabetic retinopathy screening yielded no false negative results and offered excellent image gradeability within a protocol combining nonmydriatic fundus photography and pharmacologic dilation, as needed. Image gradeability was lower with increasing patient age.