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Purpose Design Methods Results Conclusion To characterise the retinal vasculometry of a Danish eye and vision cohort and examine associations with systolic blood pressure (BP), diastolic BP, mean arterial BP, and intraocular pressure (IOP).Longitudinal study.The retinal vasculature of fundus images from the FOREVER (Finding Ophthalmic Risks and Evaluating the Value of Eye exams and their predictive Reliability) cohort was analysed using a fully automated image analysis program. Longitudinal associations of retinal vessel morphology at follow‐up visit with IOP (baseline and follow‐up) and BP (follow‐up) were examined using multilevel linear regression models adjusting for age, sex and retinal vasculometry at baseline as fixed effects and person as random effect. Width measurements were additionally adjusted for the spherical equivalent.A total of 2089 subjects (62% female) with a mean age of 61 (standard deviation 8) years and a mean follow‐up period of 4.1 years (SD 0.6 years) were included. The mean arteriolar diameter was approximately 20% thinner than the mean venular diameter, and venules were about 21%–23% less tortuous than arterioles. BP at follow‐up was associated with decreased arteriolar diameter from baseline to follow‐up. After adjusting for baseline IOP, IOP at follow‐up was associated with increased arteriolar tortuosity above baseline (0.59%, 95% CI 0.08–1.10, p‐value 0.024).In a Danish eye and vision cohort, variations in BP and alterations in IOP over time were associated with changes in the width and tortuosity of retinal vessels. Our findings contribute novel insights into retinal vascular alterations over time. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts. Method: The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths. Results: QUARTZ's performance was evaluated on a subset of 3,682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of -0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets. Conclusion: QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources. [ABSTRACT FROM AUTHOR]

Objective: To describe the development of a platform for image collection and annotation that resulted in a multi‐sourced international image dataset of oral lesions to facilitate the development of automated lesion classification algorithms. Materials and Methods: We developed a web‐interface, hosted on a web server to collect oral lesions images from international partners. Further, we developed a customised annotation tool, also a web‐interface for systematic annotation of images to build a rich clinically labelled dataset. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions. Results: The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders and other oral lesions that were collected through MeMoSA® UPLOAD. Eight‐hundred images were annotated by seven oral medicine specialists on MeMoSA®ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3%–100%). Conclusion: This is the first description of a database with clinically labelled oral lesions. This database could accelerate the improvement of AI algorithms that can promote the early detection of high‐risk oral lesions. [ABSTRACT FROM AUTHOR]

The eye is the window through which light is transmitted and visual sensory signalling originates. It is also a window through which elements of the cardiovascular and nervous systems can be directly inspected, using ophthalmoscopy or retinal imaging. Measurements of ocular parameters may therefore offer important information on the physiology and homeostasis of these two important systems. Here we report the results of a genetic characterisation of retinal vasculature. Four genome-wide association studies performed on different aspects of retinal vasculometry phenotypes, such as arteriolar and venular tortuosity and width, found significant similarities between retinal vascular characteristics and cardiometabolic health. Our analyses identified 119 different regions of association with traits of retinal vasculature, including 89 loci associated arteriolar tortuosity, the strongest of which was rs35131825 (p = 2.00×10−108), 2 loci with arteriolar width (rs12969347, p = 3.30×10−09 and rs5442, p = 1.9E-15), 17 other loci associated with venular tortuosity and 11 novel associations with venular width. Our causal inference analyses also found that factors linked to arteriolar tortuosity cause elevated diastolic blood pressure and not vice versa. Author summary: Vessels at the back of the eye (the "retina") can be imaged easily. This paper reports on the largest genetic study of retinal vessel shape and size characteristics so far undertaken, to the best of our knowledge. Our study is novel in using an automated artificial intelligence imaging approach to distinguish between arteries and veins, and in demonstrating more genetic associations with vessel characteristics than any previous study (119 genetic loci in all). We also show that the tortuosity of retinal arteries is the most strongly genetically determined vessel characteristic (replicated remarkable well in a separate second large dataset). In addition, using a particular type of genetic analysis (so called "Mendelian Randomization") we show for the first time that the tortuosity of arteries in the retina is causally related to elevated diastolic blood pressure and not the other way around. This is important as it provides unique insights into the mechanism of elevated blood pressure and hypertension, providing pointers to novel therapeutic targets for future treatment. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to assess whether adiposity or body composition relates to microvascular characteristics of the retina, indicative of cardiometabolic function.Methods: A fully automated QUARTZ software processed retinal images from 68,550 UK Biobank participants (aged 40-69 years). Differences in retinal vessel diameter and tortuosity with body composition measures from the Tanita analyzer were obtained by using multilevel regression analyses adjusted for age, sex, ethnicity, clinic, smoking, and Townsend deprivation index.Results: Venular tortuosity and diameter increased by approximately 2% (P < 10-300 ) and 0.6 μm (P < 10-6 ), respectively, per SD increase in BMI, waist circumference index, waist-hip ratio, total body fat mass index, and fat-free mass index (FFMI). Venular associations with adiposity persisted after adjustment for FFMI, whereas associations with FFMI were weakened by FMI adjustment. Arteriolar diameter (not tortuosity) narrowing with FFMI was independent of adiposity (-0.6 μm; -0.7 to -0.4 μm per SD increment of FFMI), while adiposity associations with arteriolar diameter were largely nonsignificant after adjustment for FFMI.Conclusions: This demonstrates, on an unprecedented scale, that venular tortuosity and diameter are more strongly associated with adiposity, whereas arteriolar diameter relates more strongly to fat-free mass. Different attributes of the retinal microvasculature may reflect distinct roles of body composition and fatness on the cardiometabolic system. [ABSTRACT FROM AUTHOR]

Aims: Automated retinal image analysis using Artificial Intelligence (AI) can detect diabetic retinopathy as accurately as human graders, but it is not yet licensed in the NHS Diabetic Eye Screening Programme (DESP) in England. This study aims to assess perceptions of People Living with Diabetes (PLD) and Healthcare Practitioners (HCP) towards AI's introduction in DESP., Methods: Two online surveys were co-developed with PLD and HCP from a diverse DESP in North East London. Surveys were validated through interviews across three centres and distributed via DESP centres, charities, and the British Association of Retinal Screeners. A coding framework was used to analyse free-text responses., Results: 387 (24%) PLD and 98 (37%) HCP provided comments. Themes included trust, workforce impact, the patient-practitioner relationship, AI implementation challenges, and inequalities. Both groups agreed AI in DESP was inevitable, would improve efficiency, and save costs. Concerns included job losses, data security, and AI decision safety. A common misconception was that AI would directly affect patient interactions, though it only processes retinal images., Conclusions: Limited understanding of AI was a barrier to acceptance. Educating diverse PLD groups and HCP about AI's accuracy and reliability is crucial to building trust and facilitating its integration into screening practices., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)