On-Chip Imaging of Schistosoma haematobium Eggs in Urine for Diagnosis by Computer Vision

Background: Microscopy, being relatively easy to perform at low cost, is the universal diagnostic method for detection of most globally important parasitic infections. As quality control is hard to maintain, misdiagnosis is common, which affects both estimates of parasite burdens and patient care. Novel techniques for high-resolution imaging and image transfer over data networks may offer solutions to these problems through provision of education, quality assurance and diagnostics. Imaging can be done directly on image sensor chips, a technique possible to exploit commercially for the development of inexpensive “mini-microscopes”. Images can be transferred for analysis both visually and by computer vision both at point-of-care and at remote locations. Methods/Principal Findings: Here we describe imaging of helminth eggs using mini-microscopes constructed from webcams and mobile phone cameras. The results show that an inexpensive webcam, stripped off its optics to allow direct application of the test sample on the exposed surface of the sensor, yields images of Schistosoma haematobium eggs, which can be identified visually. Using a highly specific image pattern recognition algorithm, 4 out of 5 eggs observed visually could be identified. Conclusions/Significance: As proof of concept we show that an inexpensive imaging device, such as a webcam, may be easily modified into a microscope, for the detection of helminth eggs based on on-chip imaging. Furthermore, algorithms for helminth egg detection by machine vision can be generated for automated diagnostics. The results can be exploited for constructing simple imaging devices for low-cost diagnostics of urogenital schistosomiasis and other neglected tropical infectious diseases. Author Summary: There is a need to develop diagnostic methods for parasitic infections specifically designed for use in resource-deficient situations. Worm infections are common in many poor countries and even if repeated treatment can be arranged at low cost, diagnostics and identification of treatment failures demand resources not easily available. With the proliferation of mobile phones, data transfer networks and digital microscopy applications the stage is set for alternatives to conventional microscopy in endemic areas. Our aim was to show, as proof of concept, that it is possible to achieve point-of-care diagnostics by an inexpensive mini-microscope for direct visualization on a display and remote diagnostics by computer vision. The results show that parasitic worm eggs can be recognized by on-chip imaging using a webcam stripped off the optics. Images of eggs from the blood fluke S. haematobium present in urine of an infected patient could be interpreted visually and by computer vision. The method offers both an inexpensive alternative to conventional microscopy and diagnostic assistance by computer vision.