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Background/AimsSupport vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradable with no DR versus manual grading required. The study aim was to develop a deep learning-based autograder using images and gradings from DES and to compare its performance with that of iGradingM.MethodsRetinal images, quality assurance (QA) data and routine DR grades were obtained from national datasets in 179 944 patients for years 2006–2016. QA grades were available for 744 images. We developed a deep learning-based algorithm to detect whether either eye contained ungradable images or any DR. The sensitivity and specificity were evaluated against consensus QA grades and routine grades.ResultsImages used in QA which were ungradable or with DR were detected by deep learning with better specificity compared with manual graders (p<0.001) and with iGradingM (p<0.001) at the same sensitivities. Any DR according to the DES final grade was detected with 89.19% (270 392/303 154) sensitivity and 77.41% (500 945/647 158) specificity. Observable disease and referable disease were detected with sensitivities of 96.58% (16 613/17 201) and 98.48% (22 600/22 948), respectively. Overall, 43.84% of screening episodes would require manual grading.ConclusionA deep learning-based system for DR grading was evaluated in QA data and images from 11 years in 50% of people attending a national DR screening programme. The system could reduce the manual grading workload at the same sensitivity compared with the current automated grading system.

Background/aimsNational guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme.MethodsWe consider 21 346 and 247 233 people with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), respectively, each contributing on average 4.8 and 4.4 screening intervals of which 1339 and 4675 intervals concluded with a referable screening episode. Information extracted from fundus images using DL was used to predict referable status at the end of interval and its predictive value in comparison to screening-assigned DR grade was assessed.ResultsThe DL predictor increased the area under the receiver operating characteristic curve in comparison to a predictor using current DR grades from 0.809 to 0.87 for T1DM and from 0.825 to 0.87 for T2DM. Expected sojourn time—the time from becoming referable to being rescreened—was found to be 3.4 (T1DM) and 2.7 (T2DM) weeks less for a DL-derived policy compared with the current recall policy.ConclusionsWe showed that, compared with using the current retinopathy grade, DL of fundus images significantly improves the prediction of incident referable retinopathy before the next screening episode. This can impact screening recall interval policy positively, for example, by reducing the expected time with referable disease for a fixed workload—which we show as an exemplar. Additionally, it could be used to optimise workload for a fixed sojourn time.

Diabetes is a major public health issue. Because lifetime risk, life expectancy, and years of life lost are meaningful metrics for clinical decision making, we aimed to estimate these measures for type 2 diabetes in the high-income setting.

We aimed to ascertain the cumulative risk of fatal or critical care unit-treated COVID-19 in people with diabetes and compare it with that of people without diabetes, and to investigate risk factors for and build a cross-validated predictive model of fatal or critical care unit-treated COVID-19 among people with diabetes.

Despite a lack of supporting evidence, current guidance recommends against the use of metformin in people with advanced kidney impairment. This observational study compared the outcomes of patients with type 2 diabetes who continued versus stopped metformin after developing stage 4 chronic kidney disease (CKD) (estimated glomerular filtration rate [eGFR]<30mL/min/1.73m2).

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Davies R., McGurnaghan M. A. and Sams K. I. (1977) The Northern Ireland economy: progress (1968-75) and prospects, Reg. Studies 11, 297-307. Unemployment - public spending and public services - emigration.This paper considers how the political disturbances in Northern Ireland affected economic performance in the period 1968-75. The conclusions it reaches are that the unemployment situation had not worsened as might have been expected because of the influence of increased public spending and higher emigration. The most important factor has been an increase in employment in the public services which seems to have been an unintended consequence of the rise in government spending. But, due to cuts in public expenditure, this cannot continue and the future outlook for employment is bleak.