Aims-To compare methods available to correct the magnification of images that result from the optics of the eye and identify errors, and source of error, of the methods. Methods-11 methods were applied to ocular biometry data from three independent cohorts. Each method was compared with the method of Bennett, which uses most biometric data. The diVerence between each method and Bennett's is the "error" of the method. The relation between the error and axial length, ametropia, and keratometry was explored by linear regression analysis. Results-Methods using axial length had the lowest mean (+0.5 to +2.6%) and standard deviation (0.6 to 1.2%) of errors. Of methods using keratometry and ametropia only, the lowest mean (−1.4% to +4.4%) and standard deviation (2.9 to 4.3%) of errors was found for a new method described in this paper, and that used by the Heidelberg retina tomograph (HRT). The highest mean error (+2.2 to +7.1%) was found for Littmann's method. Littmann's correction was larger than the HRT's by 3.5 to 3.7%. The mean difference between the new and HRT methods and the "abbreviated axial length" method of Bennett is −1.3 to +2.0%. The error of the "keratometry and ametropia" methods is related to axial length. Conclusions-Methods using axial length are most accurate. The abbreviated axial length method of Bennett diVers little from more detailed calculations and is appreciably more accurate than methods using keratometry and ametropia alone. If axial length is unknown, the new and the HRT methods give results closest to the abbreviated axial length method. (Br J Ophthalmol 1998;82:643-649)

Genetic and epidemiologic studies have shown that lipid genes and High Density Lipoproteins (HDL) are implicated in age-related macular degeneration (AMD). We studied circulating lipid levels in relation to AMD in a large European dataset, and investigated whether this relationship is driven by certain sub fractions. Design: (Pooled) analysis of cross-sectional data. Participants: 30,953 individuals aged 50+ participating in the E3 consortium; and 1530 individuals from the Rotterdam Study with lipid sub fraction data. Methods: In E3, AMD features were graded per eye on fundus photographs using the Rotterdam Classification. Routine blood lipid measurements were available from each participant. Data on genetics, medication and confounders such as body mass index, were obtained from a common database. In a subgroup of the Rotterdam Study, lipid sub fractions were identified by the Nightingale biomarker platform. Random-intercepts mixed-effects models incorporating confounders and study site as a random-effect were used to estimate the associations. Main Outcome Measures: early, late or any AMD, phenotypic features of early AMD, lipid measurements. Results: HDL was associated with an increased risk of AMD, corrected for potential confounders (Odds Ratio (OR) 1.21 per 1mmol/L increase (95% confidence interval[CI] 1.14-1.29); while triglycerides were associated with a decreased risk (OR 0.94 per 1mmol/L increase [95%CI 0.91-0.97]). Both were associated with drusen size, higher HDL raises the odds of larger drusen while higher triglycerides decreases the odds. LDL-cholesterol only reached statistical significance in the association with early AMD (p=0.045). Regarding lipid sub fractions: the concentration of extra-large HDL particles showed the most prominent association with AMD (OR 1.24 [95%CI 1.10-1.40]). The CETP risk variant (rs17231506) for AMD was in line with increased-HDL levels (p=7.7x10-7); but LIPC risk variants (rs2043085, rs2070895) were associated in an opposite way (p=1.0x10-6 and 1.6x10-4). Conclusions: Our study suggests that HDL-cholesterol is associated with increased risk of AMD and triglycerides negatively associated. Both show the strongest association with early AMD and drusen. Extra-large HDL sub fractions seem to be drivers in the relation with AMD, variants in lipid genes play a more ambiguous role in this association. Whether systemic lipids directly influence AMD or represent lipid metabolism in the retina remains a question to be answered.

Background/aimsHuman grading of digital images from diabetic retinopathy (DR) screening programmes represents a significant challenge, due to the increasing prevalence of diabetes. We evaluate the performance of an automated artificial intelligence (AI) algorithm to triage retinal images from the English Diabetic Eye Screening Programme (DESP) into test-positive/technical failure versus test-negative, using human grading following a standard national protocol as the reference standard.MethodsRetinal images from 30 405 consecutive screening episodes from three English DESPs were manually graded following a standard national protocol and by an automated process with machine learning enabled software, EyeArt v2.1. Screening performance (sensitivity, specificity) and diagnostic accuracy (95% CIs) were determined using human grades as the reference standard.ResultsSensitivity (95% CIs) of EyeArt was 95.7% (94.8% to 96.5%) for referable retinopathy (human graded ungradable, referable maculopathy, moderate-to-severe non-proliferative or proliferative). This comprises sensitivities of 98.3% (97.3% to 98.9%) for mild-to-moderate non-proliferative retinopathy with referable maculopathy, 100% (98.7%,100%) for moderate-to-severe non-proliferative retinopathy and 100% (97.9%,100%) for proliferative disease. EyeArt agreed with the human grade of no retinopathy (specificity) in 68% (67% to 69%), with a specificity of 54.0% (53.4% to 54.5%) when combined with non-referable retinopathy.ConclusionThe algorithm demonstrated safe levels of sensitivity for high-risk retinopathy in a real-world screening service, with specificity that could halve the workload for human graders. AI machine learning and deep learning algorithms such as this can provide clinically equivalent, rapid detection of retinopathy, particularly in settings where a trained workforce is unavailable or where large-scale and rapid results are needed.

To examine the baseline associations of retinal vessel morphometry with blood pressure (BP) and arterial stiffness in United Kingdom Biobank. The United Kingdom Biobank included 68 550 participants aged 40 to 69 years who underwent nonmydriatic retinal imaging, BP, and arterial stiffness index assessment. A fully automated image analysis program (QUARTZ [Quantitative Analysis of Retinal Vessel Topology and Size]) provided measures of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiovascular disease risk factors/outcomes were examined using multilevel linear regression to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing within person clustering), adjusted for age, sex, ethnicity, clinic, body mass index, smoking, and deprivation index. Greater arteriolar tortuosity was associated with higher systolic BP (relative increase, 1.2%; 95% CI, 0.9; 1.4% per 10 mmHg), higher mean arterial pressure, 1.3%; 0.9, 1.7% per 10 mmHg, and higher pulse pressure (PP, 1.8%; 1.4; 2.2% per 10 mmHg). Narrower arterioles were associated with higher systolic BP (−0.9 µm; −0.94, −0.87 µm per 10 mmHg), mean arterial pressure (−1.5 µm; −1.5, −1.5 µm per 10 mmHg), PP (−0.7 µm; −0.8, −0.7 µm per 10 mmHg), and arterial stiffness index (−0.12 µm; −0.14, −0.09 µm per ms/m 2 ). Associations were in the same direction but marginally weaker for venular tortuosity and diameter. This study assessing the retinal microvasculature at scale has shown clear associations between retinal vessel morphometry, BP, and arterial stiffness index. These observations further our understanding of the preclinical disease processes and interplay between microvascular and macrovascular disease.