Richard J Maude
Professor Maude is Head of Epidemiology at Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand and Associate Professor in Tropical Medicine at the University of Oxford, Honorary Consultant Physician at the John Radcliffe Hospital in Oxford and a Visiting Scientist at Harvard TH Chan School of Public Health, Harvard University, Boston, USA. He has worked at Mahidol-Oxford Tropical Medicine Research Unit since 2007.
His research combines clinical studies, descriptive epidemiology and mathematical modelling of communicable diseases in South and Southeast Asia. His areas of interest include spatiotemporal epidemiology, GIS mapping and population movement, malaria parasite genetics, population dynamic mathematical modelling of artemisinin resistance and malaria elimination, and clinical studies on severe malaria pathogenesis and treatment, malaria diagnosis and antimalarial drug resistance.
Address: Mahidol-Oxford Tropical Medicine Research Unit,
Faculty of Tropical Medicine, Mahidol University,
Bangkok, Thailand
His research combines clinical studies, descriptive epidemiology and mathematical modelling of communicable diseases in South and Southeast Asia. His areas of interest include spatiotemporal epidemiology, GIS mapping and population movement, malaria parasite genetics, population dynamic mathematical modelling of artemisinin resistance and malaria elimination, and clinical studies on severe malaria pathogenesis and treatment, malaria diagnosis and antimalarial drug resistance.
Address: Mahidol-Oxford Tropical Medicine Research Unit,
Faculty of Tropical Medicine, Mahidol University,
Bangkok, Thailand
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Papers by Richard J Maude
malarial retinopathy, which may help in the assessment of
patients with cerebral malaria.
METHODS. A fundus image dataset from 14 patients (200 fundus images, with an average of 14 images per patient) previously diagnosed with malarial retinopathy was examined. We developed a pattern recognition–based algorithm, which
extracted features from image watershed regions called splats
(tobogganing). A reference standard was obtained by manual
segmentation of hemorrhages, which assigned a label to each
splat. The splat features with the associated splat label were
used to train a linear k-nearest neighbor classifier that learnt
the color properties of hemorrhages and identified the splats
belonging to hemorrhages in a test dataset. In a crossover
design experiment, data from 12 patients were used for
training and data from two patients were used for testing, with
14 different permutations; and the derived sensitivity and
specificity values were averaged.
RESULTS. The experiment resulted in hemorrhage detection
sensitivities in terms of splats as 80.83%, and in terms of lesions as 84.84%. The splat-based specificity was 96.67%, whereas for the lesion-based analysis, an average of three false positives was obtained per image. The area under the receiver operating characteristic curve was reported as 0.9148 for splat-based, and as 0.9030 for lesion-based analysis.
malarial retinopathy, which may help in the assessment of
patients with cerebral malaria.
METHODS. A fundus image dataset from 14 patients (200 fundus images, with an average of 14 images per patient) previously diagnosed with malarial retinopathy was examined. We developed a pattern recognition–based algorithm, which
extracted features from image watershed regions called splats
(tobogganing). A reference standard was obtained by manual
segmentation of hemorrhages, which assigned a label to each
splat. The splat features with the associated splat label were
used to train a linear k-nearest neighbor classifier that learnt
the color properties of hemorrhages and identified the splats
belonging to hemorrhages in a test dataset. In a crossover
design experiment, data from 12 patients were used for
training and data from two patients were used for testing, with
14 different permutations; and the derived sensitivity and
specificity values were averaged.
RESULTS. The experiment resulted in hemorrhage detection
sensitivities in terms of splats as 80.83%, and in terms of lesions as 84.84%. The splat-based specificity was 96.67%, whereas for the lesion-based analysis, an average of three false positives was obtained per image. The area under the receiver operating characteristic curve was reported as 0.9148 for splat-based, and as 0.9030 for lesion-based analysis.