Digital holography integrated with flow cytometry for detection of urinary schistosomiasis

Schistosomiasis is an intravascular infection with major public health consequences in developing countries. It is one of the major Neglected Tropical Diseases with more than 240 million people infected and 800 million people at risk in 2015, mostly in sub-Saharan Africa. It is caused by trematode parasites of the genus Schistosoma, in this report the focus was on Schistosomiasis Haematobium since it is the most prevalent form of the disease. One of the limiting factors of the control program is the standard diagnostic procedure set by World Health Organization, which is based on counting the parasite's eggs in a person's urine. Examination by microscopy requires the use of expensive microscopes, is prone to human errors and inconsistency, is time consuming, and uses filters which are often not available. The research objective was identified from these shortcomings: "Develop a low-cost, smart diagnostic method for Schistosomiasis Haematobium based on detecting eggs in urine by combining lensless imaging and flow cytometry, and developing Artificial Intelligence models for automated detection." In-line planar wavefront digital holography was identified as the most suitable lensless imaging method. A sample will be analyzed by the following repetitive procedure: (1)mechanically press the piston of a syringe by a small volume, (2)wait for the flow to stop, (3)record a hologram, (4)detect eggs. The implemented egg detection procedure consisted of a series of image processing algorithms: (1)apply Foreground detection, (2)localize the moving objects with a Blob detector, (3)locally reconstruct the hologram at the found locations, (4)classify the reconstruction as egg or not egg. The imaging method provided accurate reconstructions of eggs and the object detection algorithm was able to locate moving objects with sufficient accuracy and computational time. On the other hand, the lab and field tests showed that the data set of the classifier did not contain enough images to train a generalized model and that the local reconstruction and classification takes increasingly more time during analysis. As of now the method is an order of magnitude slower than an expert microscopist. The diagnostic method is not yet able fulfill the research objective. However, there are some promising aspects such as the low-cost imaging method, fast object detection algorithm, and absence of sample preparation which makes further research worthwhile.