AIMC Topic: Schistosomiasis haematobia
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Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.
PLoS neglected tropical diseases
Nov 5, 2024
BACKGROUND: Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to affect 150 million people worldwide, with a high relevance in resource-poor settings of the African con...
Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030.
The American journal of tropical medicine and hygiene
May 21, 2024
The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public health issue, targeting reductions in the heavy intensity of infections. Previous studies, however, have predominantly used prevalence as the primary indic...
Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.
PLoS neglected tropical diseases
Feb 23, 2024
INTRODUCTION: Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microsco...
Egg Excretion does not Increase after Exercise: Implications for Diagnostic Testing.
The American journal of tropical medicine and hygiene
Dec 14, 2017
Children are frequently invited to exercise before micturition, as it is believed that this activity will result in higher egg excretion, and hence, increases sensitivity of microscopic diagnoses. However, the evidence of this recommendation is scan...
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