For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings. @en

Significance: Particle field holography is a versatile technique to determine the size and distribution of moving or stationary particles in air or in a liquid without significant disturbance of the sample volume. Although this technique is applied in biological sample analysis, it is limited to small sample volumes, thus increasing the number of measurements per sample. In this work, we characterize the maximum achievable volume limit based on the specification of a given sensor to realize the development of a potentially low-cost, single-shot, large-volume holographic microscope.

Aim: We present mathematical formulas that will aid in the design and development and improve the focusing speed for the numerical reconstruction of registered holograms in particle field holographic microscopes. Our proposed methodology has potential application in the detection of Schistosoma haematobium eggs in human urine samples.

Approach: Using the Fraunhofer holography theory for opaque objects, we derived an exact formula for the maximum diffraction-limited volume for an in-line holographic setup. The proof-of-concept device built based on the derived formulas was experimentally validated with urine spiked with cultured Schistosoma haematobium eggs.

Results: Results obtained show that for urine spiked with Schistosoma haematobium eggs, the volume thickness is limited to several millimeters due to scattering properties of the sample. The distances of the target particles could be estimated directly from the hologram fringes.

Conclusion: The methodology proposed will aid in the development of large-volume holographic microscopes.@en

We present a simple method for the diagnosis of urinary schistosomiasis using an in-line lensless holographic microscope combined with flow cytometry technique. Using simple image processing algorithms and binary image classifier, our system provides automated detection of Schistosoma haematobium eggs in infected urine samples. Registered hologram is reconstructed by applying backpropagation from sensor to sample plane and reconstructed image is automatically analysed for the presence of S. haematobium eggs. Designed for use in a resource-poor laboratory setting, our proposed method has been implemented using a Raspberry Pi computer. From pre-clinical test performed with human urine samples spiked with S. haematobium eggs (approximately 200 eggs per 12 ml of urine), we achieved a sensitivity and specificity of 50.6% and 98.6% respectively. Our proposed method requires no complex sample preparation methods making the system simple to operate and useable in point-of-care diagnosis of urinary schistosomiasis.This method can be optimized to complement existing diagnostic procedures for the detection of S. haematobium eggs and can be deployed to inaccessible remote areas.@en