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.

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Schistosomiasis is a Neglected Tropical Disease, prevailing mostly in poor and rural communities in sub-Saharan Africa, particularly those that heavily rely on water bound occupations, such as fishing and specific agriculture. In 2019, a total of 236.6 million people required treatment for schistosomiasis and only 1 million received it. Without treatment people are vulnerable for chronic infections and eventually death. Accurate diagnosis is crucial to reduce risks of chronic infections. Schistosomiasis is currently diagnosed through the detection of parasite eggs in stool or urine specimens. Eggs are detected and counted by a trained microscopist examining the filter on a glass slide. This is difficult and hard work, requiring a non-ergonomic posture and medical training. Only a limited group of people can properly receive this training, due to a lack of resources for education. As an alternative the INSPIRED Project has developed the Schistoscope to automate this process to assist the microscopist. The current prototype, the Schistoscope 4 is an automated digital microscope that analyses images through Artificial Intelligence to provide diagnosis. In 2021, the research group will execute a field study in Nigeria. The goal is to collect samples in highly infected communities and compare the work of the Schistoscope 5 to a standard microscopist. The Schistoscope 4 is considered insufficient for executing this field study. Consequently, an improved device is required. An analysis on previous versions of the Schistoscope provided challenges for improvement. The challenges addressed in this graduation project are: (1) improve the stability and fixation of the camera. (2) Include the illumination in the optical set-up of the device and (3) allow for a change of view received by the camera. Furthermore, (4) the view of the camera must consistent and reliable. At last, (5) all systems must be integrated in to a stable embodiment, (6) protected from the outside environment. The graduation project uses a PDCA-cycle approach combined with systems engineering to structure the design process. With this iterative approach, the requirements are continuously evaluated and updated. The final design presented in this thesis provides the research group with reliable and sufficient quality results. A stable construction with aluminium profiles allows for the required adaptability needed because of the dynamic nature of the research project. Additionally, the aluminium profiles provide easy attachment of numerous off-the-shelf components. These off-the-shelf components provide consistent movement of the sample and optical system. They allow for changes in electronic hardware and overthrow the initial 3D-printed production method. An enclosure system integrates all systems inside the device and protects these systems from impacts from the environment. During the project, a total of three prototypes are produced, of which two devices are successfully transported to Nigeria, where they are operational in the field test laboratory. The Schistoscope 5 is a clear improvement of the previous prototypes and excels in stability of the components, reliability of movement and consistency in quality.