Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.

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 continent. The gold-standard diagnosis is still direct observation of Schistosoma haematobium eggs in urine samples by optical microscopy.

Real-time PCR for malaria diagnosis and identification of Plasmodium species in febrile patients in Cubal, Angola.

Background: Malaria is the parasitic disease with the highest morbimortality worldwide. The World Health Organization (WHO) estimates that there were approximately 249 million cases in 2022, of which 3.4% were in Angola.

Usefulness of real-time PCR for urogenital schistosomiasis diagnosis in preschool children in a high-prevalence area in Angola.

Background: Urogenital schistosomiasis caused by Schistosoma haematobium is highly endemic in the municipality of Cubal in Angola. Currently, diagnosis is based on the observation of S. haematobium eggs in urine samples by microscopy but this method has low sensitivity.