[(18)F]-FDG PET imaging of primary and metastatic breast cancer after a single pulse of chemotherapy may be of value in the prediction of pathologic treatment response.

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%.

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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.

An automated technique was developed to detect retinopathy in digital red-free fundus images that can form part of a diabetic retinopathy screening programme. It is believed that it can perform a useful role in this context identifying images worthy of closer inspection or eliminating 50% or more of the screening population who have no retinopathy.