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The Influence of Age, Duration of Diabetes, Cataract, and Pupil Size on Image Quality in Digital Photographic Retinal Screening Peter Henry Scanlon , MRCP 1 , Chris Foy , MSC 2 , Raman Malhotra , FRCO, PHTH 3 and Stephen J. Aldington , DMS 4 1 Department of Ophthalmology, Cheltenham General Hospital, Cheltenham, U.K. 2 R&D Support Unit, Gloucester Hospitals National Health Service Trust, Gloucester, U.K. 3 Oxford Eye Hospital, Oxford, U.K. 4 Retinopathy Grading Centre, Imperial College, London, U.K. Address correspondence and reprint requests to Dr. Peter Scanlon, Gloucestershire Eye Unit, Cheltenham General Hospital, Sandford Road, Cheltenham, GL53 7AN, U.K. E-mail: peter.scanlon{at}glos.nhs.uk Abstract OBJECTIVE —To evaluate the effect of age, duration of diabetes, cataract, and pupil size on the image quality in digital photographic screening. RESEARCH DESIGN AND METHODS —Randomized groups of 3,650 patients had one-field, nonmydriatic, 45° digital retinal imaging photography before mydriatic two-field photography. A total of 1,549 patients were then examined by an experienced ophthalmologist. Outcome measures were ungradable image rates, age, duration of diabetes, detection of referable diabetic retinopathy, presence of early or obvious central cataract, pupil diameter, and iris color. RESULTS —The ungradable image rate for nonmydriatic photography was 19.7% (95% CI 18.4–21.0) and for mydriatic photography was 3.7% (3.1–4.3). The odds of having one eye ungradable increased by 2.6% (1.6–3.7) for each extra year since diagnosis for nonmydriatic, by 4.1% (2.7–5.7) for mydriatic photography irrespective of age, by 5.8% (5.0–6.7) for nonmydriatic, and by 8.4% (6.5–10.4) for mydriatic photography for every extra year of age, irrespective of years since diagnosis. Obvious central cataract was present in 57% of ungradable mydriatic photographs, early cataract in 21%, no cataract in 9%, and 13% had other pathologies. The pupil diameter in the ungradable eyes showed a significant trend ( P < 0.001) in the three groups (obvious cataract 4.434, early cataract 3.379, and no cataract 2.750). CONCLUSIONS —The strongest predictor of ungradable image rates, both for nonmydriatic and mydriatic digital photography, is the age of the person with diabetes. The most common cause of ungradable images was obvious central cataract. Footnotes A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances. Accepted June 23, 2005. Received February 9, 2005. DIABETES CARE

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.

Background /AimsTo evaluate the performance of existing prediction models to determine risk of progression to referable diabetic retinopathy (RDR) using data from a prospective Irish cohort of people with type 2 diabetes (T2D).MethodsA cohort of 939 people with T2D followed prospectively was used to test the performance of risk prediction models developed in Gloucester, UK, and Iceland. Observed risk of progression to RDR in the Irish cohort was compared with that derived from each of the prediction models evaluated. Receiver operating characteristic curves assessed models’ performance.ResultsThe cohort was followed for a total of 2929 person years during which 2906 screening episodes occurred. Among 939 individuals followed, there were 40 referrals (4%) for diabetic maculopathy, pre-proliferative DR and proliferative DR. The original Gloucester model, which includes results of two consecutive retinal screenings; a model incorporating, in addition, systemic biomarkers (HbA1c and serum cholesterol); and a model including results of one retinopathy screening, HbA1c, total cholesterol and duration of diabetes, had acceptable discriminatory power (area under the curve (AUC) of 0.69, 0.76 and 0.77, respectively). The Icelandic model, which combined retinopathy grading, duration and type of diabetes, HbA1c and systolic blood pressure, performed very similarly (AUC of 0.74).ConclusionIn an Irish cohort of people with T2D, the prediction models tested had an acceptable performance identifying those at risk of progression to RDR. These risk models would be useful in establishing more personalised screening intervals for people with T2D.

Aims: 1) To validate an Ophthalmologists reference standard examination. 2) To evaluate the effectiveness and cost-effectiveness of the introduction of a community based non-mydriatic and mydriatic digital photographic screening programme for diabetic retinopathy. 3) To determine the subsequent change in workload of the Ophthalmology Department. Methods: 1) An Ophthalmologist's examination was compared prospectively with 7-field stereo-photography in 239 persons. 2) 3611 patients attending general practices in Gloucestershire had one-field, non-mydriatic and mydriatic two-field digital photography. 1549 of these patients were examined by an Ophthalmologist. A cost effectiveness analysis was undertaken. 3) A retrospective study of Eye clinic workload was performed for the year before screening commenced, 2 years of the first round and the first year of the second round. Results: 1) In comparison with 7-field stereo photography, the Ophthalmologist's examination gave a sensitivity of 87.4% (confidence interval 83.5-91.5%) and a specificity of 94.9% (91.5-98.3%). 2) For mydriatic digital photography, the sensitivity was 87.8%, specificity was 86.1% and technical failure rate was 3.7%. For non-mydriatic photography, the sensitivity was 86.0%, specificity was 76.7% and technical failure rate was 19.7%. The best estimate of cost per true positive detected was 429 (range 394-473) for mydriatic and 490 ( 450-535) for non-mydriatic photography. 3) The annual referral rate and the number with diabetes in the county increased over the four years and only reduced in the fourth year for laser treatment sessions (171,282, 265, 199). Conclusions: Two-field mydriatic digital photography is an effective and cost-effective method of screening for referable diabetic retinopathy whereas non-mydriatic digital photography has an unacceptable technical failure rate and low specificity. The consequent workload in the Eye clinic increased in the first round of screening but, with increasing numbers of people with diabetes, did not fall below the pre-screening level, except for laser treatment.

Photovoltaics (PVs) are a critical technology for curbing growing levels of anthropogenic greenhouse gas emissions, and meeting increases in future demand for low-carbon electricity. In order to fulfil ambitions for net-zero carbon dioxide equivalent (CO2eq) emissions worldwide, the global cumulative capacity of solar PVs must increase by an order of magnitude from 0.9 TWp in 2021 to 8.5 TWp by 2050 according to the International Renewable Energy Agency, which is considered to be a highly conservative estimate. In 2020, the Henry Royce Institute brought together the UK PV community to discuss the critical technological and infrastructure challenges that need to be overcome to address the vast challenges in accelerating PV deployment. Herein, we examine the key developments in the global community, especially the progress made in the field since this earlier roadmap, bringing together experts primarily from the UK across the breadth of the photovoltaics community. The focus is both on the challenges in improving the efficiency, stability and levelized cost of electricity of current technologies for utility-scale PVs, as well as the fundamental questions in novel technologies that can have a significant impact on emerging markets, such as indoor PVs, space PVs, and agrivoltaics. We discuss challenges in advanced metrology and computational tools, as well as the growing synergies between PVs and solar fuels, and offer a perspective on the environmental sustainability of the PV industry. Through this roadmap, we emphasize promising pathways forward in both the short- and long-term, and for communities working on technologies across a range of maturity levels to learn from each other.