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Alan Fleming

Researcher at University of Aberdeen

Publications -  70
Citations -  4036

Alan Fleming is an academic researcher from University of Aberdeen. The author has contributed to research in topics: Diabetic retinopathy & Mass screening. The author has an hindex of 28, co-authored 67 publications receiving 3731 citations. Previous affiliations of Alan Fleming include University of Edinburgh & Carnegie Learning.

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Color Doppler myocardial imaging: a new technique for the assessment of myocardial function

TL;DR: The concomitant enhancement of the myocardial Doppler signal after an intravenous injection of a transpulmonary echocardiographic contrast agent could permit the noninvasive assessment of regional myocardIAL perfusion.
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Automated microaneurysm detection using local contrast normalization and local vessel detection

TL;DR: It is shown how image contrast normalization can improve the ability to distinguish between MAs and other dots that occur on the retina.
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Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes: response to Abramoff et al.

TL;DR: Comparing manual and automated grading against a reference standard grading of 14,406 images, it is found that the automated system attained a higher sensitivity for detection of patients requiring “full disease” grading than the manual system.
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Differences in Myocardial Velocity Gradient Measured Throughout the Cardiac Cycle in Patients With Hypertrophic Cardiomyopathy, Athletes and Patients With Left Ventricular Hypertrophy Due to Hypertension

TL;DR: The myocardial velocity gradient measured across the left ventricular (LV) posterior wall during the cardiac cycle between patients with hypertrophic cardiomyopathy, athletes and patients with LV hypertrophy due to systemic hypertension was compared to determine whether it might be used to discriminate these groups.
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The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme

TL;DR: Automated “disease/no disease” grading of diabetic retinopathy could safely reduce the burden of grading in diabetic Retinopathy screening programmes.