Smart diagnostics for low resource settings

My graduation project is about the diagnosis of urinary schistosomiasis. Schistosomiasis is a neglected tropical disease, which causes more than 200,000 deaths a year. The disease is spread by contact with contaminated fresh water. Snails in the water carry parasites which penetrate through the human skin. Infected people release schistosoma eggs into their urine and feces. Currently, diagnosis in low resource settings is usually done by examination of urine. Urine is filtrated or centrifuged, after which the parasites' eggs are manually counted under a microscope to determine the presence and severity of the disease. Local government areas are checked and mapped on the disease by mass screening of school children or risk groups. However, this is a time consuming process and requires highly skilled lab technicians. Diagnosis in rural areas is limited by poor infrastructure, lack of money for better medical equipment, shortage of skilled technicians and microscopes, and superstitious beliefs of communities. The involved stakeholders are the local communities, health care facilities, the government and NGO's. The last couple of years, LUMC and TU Delft developed new technologies and algorithms to diagnose schistosomiasis without microscope. This gives a great opportunity for design of diagnostic devices for remote areas, since skilled lab technicians and expensive microscopes are not needed anymore. This could make the diagnosis of schistosomiasis cheaper, more time efficient and more precise. Several project groups and graduation students developed working prototypes of diagnostic devices based on multiple working principles, but more context research is needed to develop better specifications for technology performance optimization, better distributed manufacturability and improve user interaction and repair and maintentance. a device that fits the needs of the stakeholders and fills a gap where there is currently no diagnostic method suitable.