Aim: To assess the efficacy of automated ''disease/no disease'' grading for diabetic retinopathy within a systematic screening programme. Methods: Anonymised images were obtained from consecutive patients attending a regional primary care based diabetic retinopathy screening programme. A training set of 1067 images was used to develop automated grading algorithms. The final software was tested using a separate set of 14 406 images from 6722 patients. The sensitivity and specificity of manual and automated systems operating as ''disease/no disease'' graders (detecting poor quality images and any diabetic retinopathy) were determined relative to a clinical reference standard. Results: The reference standard classified 8.2% of the patients as having ungradeable images (technical failures) and 62.5% as having no retinopathy. Detection of technical failures or any retinopathy was achieved by manual grading with 86.5% sensitivity (95% confidence interval 85.1 to 87.8) and 95.3% specificity (94.6 to 95.9) and by automated grading with 90.5% sensitivity (89.3 to 91.6) and 67.4% specificity (66.0 to 68.8). Manual and automated grading detected 99.1% and 97.9%, respectively, of patients with referable or observable retinopathy/maculopathy. Manual and automated grading detected 95.7% and 99.

Screening programs using retinal photography for the detection of diabetic eye disease are being introduced in the UK and elsewhere. Automatic grading of the images is being considered by health boards so that the human grading task is reduced. Microaneurysms (MAs) are the earliest sign of this disease and so are very important for classifying whether images show signs of retinopathy. This paper describes automatic methods for MA detection and shows how image contrast normalization can improve the ability to distinguish between MAs and other dots that occur on the retina. Various methods for contrast normalization are compared. Best results were obtained with a method that uses the watershed transform to derive a region that contains no vessels or other lesions. Dots within vessels are handled successfully using a local vessel detection technique. Results are presented for detection of individual MAs and for detection of images containing MAs. Images containing MAs are detected with sensitivity 85.4% and specificity 83.1%.

The automated system performs with sufficient accuracy to form part of an automated diabetic retinopathy grading system.

Both manual grading methods produced similar results whether using a one- or two-field protocol. Technical failures rates, and hence need for recall, were lower with digital imaging. One-field grading of fundus photographs appeared to be as effective as two-field. The optometrists achieved the lowest sensitivities but reported no technical failures. Automated grading of retinal images can improve efficiency of resource utilization in diabetic retinopathy screening.

Aims: National screening programmes for diabetic retinopathy using digital photography and multi-level manual grading systems are currently being implemented in the UK. Here, we assess the cost-effectiveness of replacing first level manual grading in the National Screening Programme in Scotland with an automated system developed to assess image quality and detect the presence of any retinopathy. Methods: A decision tree model was developed and populated using sensitivity/specificity and cost data based on a study of 6722 patients in the Grampian region. Costs to the NHS, and the number of appropriate screening outcomes and true referable cases detected in 1 year were assessed. Results: For the diabetic population of Scotland (approximately 160 000), with prevalence of referable retinopathy at 4% (6400 true cases), the automated strategy would be expected to identify 5560 cases (86.9%) and the manual strategy 5610 cases (87.7%). However, the automated system led to savings in grading and quality assurance costs to the NHS of £201 600 per year. The additional cost per additional referable case detected (manual vs automated) totalled £4088 and the additional cost per additional appropriate screening outcome (manual vs automated) was £1990. Conclusions: Given that automated grading is less costly and of similar effectiveness, it is likely to be considered a cost-effective alternative to manual grading. S ystematic screening for diabetic retinopathy has been identified as a cost-effective use of health service resources.1-4 The Health Technology Board for Scotland recommended a national screening programme using digital photography and a multi-level manual grading system (fig 1), which is currently being implemented in Scotland.5 Similar programmes are also underway in England, Wales and Northern Ireland.With 161 946 individuals recorded on diabetes registers in Scotland, 6 manual grading is a resource-intensive activity. Current policy is implemented by capturing digital images at local screening centres, which are then sent electronically to one of nine regional grading centres. However, a system of automated grading could provide cost savings to the NHS. Our research group recently developed and evaluated an automated grading system that can assess digital retinal images for quality 8 and the presence of retinopathy. 9This system could thereby potentially replace manual level 1 grading. The purpose of this paper is to assess the costeffectiveness of replacing this manual disease/no disease grading with the automated system, in the context of the three-level grading system used in Scotland (fig 1). A decision tree model was developed to compare NHS grading costs and screening outcomes over a one-year period for these two alternative strategies.

Given the repeated failure of amyloid-based approaches in Alzheimer’s disease, there is increasing interest in tau-based therapeutics. Although methylthioninium (MT) treatment was found to be beneficial in tau transgenic models, the brain concentrations required to inhibit tau aggregation in vivo are unknown. The comparative efficacy of methylthioninium chloride (MTC) and leucomethylthioninium salts (LMTX; 5–75 mg/kg; oral administration for 3–8 weeks) was assessed in two novel transgenic tau mouse lines. Behavioural (spatial water maze, RotaRod motor performance) and histopathological (tau load per brain region) proxies were applied. Both MTC and LMTX dose-dependently rescued the learning impairment and restored behavioural flexibility in a spatial problem-solving water maze task in Line 1 (minimum effective dose: 35 mg MT/kg for MTC, 9 mg MT/kg for LMTX) and corrected motor learning in Line 66 (effective doses: 4 mg MT/kg). Simultaneously, both drugs reduced the number of tau-reactive neurons, particularly in the hippocampus and entorhinal cortex in Line 1 and in a more widespread manner in Line 66. MT levels in the brain followed a sigmoidal concentration–response relationship over a 10-fold range (0.13–1.38 μmol/l). These data establish that diaminophenothiazine compounds, like MT, can reverse both spatial and motor learning deficits and reduce the underlying tau pathology, and therefore offer the potential for treatment of tauopathies.

Screening programmes for diabetic retinopathy are being introduced in the United Kingdom and elsewhere. These require large numbers of retinal images to be manually graded for the presence of disease. Automation of image grading would have a number of benefits. However, an important prerequisite for automation is the accurate location of the main anatomical features in the image, notably the optic disc and the fovea. The locations of these features are necessary so that lesion significance, image field of view and image clarity can be assessed. This paper describes methods for the robust location of the optic disc and fovea. The elliptical form of the major retinal blood vessels is used to obtain approximate locations, which are refined based on the circular edge of the optic disc and the local darkening at the fovea. The methods have been tested on 1056 sequential images from a retinal screening programme. Positional accuracy was better than 0.5 of a disc diameter in 98.4% of cases for optic disc location, and in 96.5% of cases for fovea location. The methods are sufficiently accurate to form an important and effective component of an automated image grading system for diabetic retinopathy screening.

Background/aims: Automated grading software has the potential to reduce the manual grading workload within diabetic retinopathy screening programmes. This audit was undertaken at the request of Scotland's National Diabetic Retinopathy Screening Collaborative to assess whether the introduction of automated grading software into the national screening programme would be safe, robust and effective.Methods: Automated grading, performed by software for image quality assessment and for microaneurysm/dot haemorrhage detection, was carried out on 78,601 images, obtained from 33,535 consecutive patients, which had been manually graded at one of two regional diabetic retinopathy screening programmes. Cases where the automated grading software assessment indicated gradable images with no disease but the screening programme indicated ungradable images or disease more severe than mild retinopathy were arbitrated by 7 senior ophthalmologists. Conclusion:The automated grading software operated to previously published results when applied to a large, unselected population attending two regional screening programmes. Manual grading workload reduction would be 36.3%. INTRODUCTIONSystematic screening for diabetic retinopathy using retinal photography has been shown to reduce the incidence of blindness among people with diabetes.