02686nas a2200301   4500000000100000008004100001260001200042653004200054653002700096653003600123100001200159700001800171700001300189700001300202700001500215700001700230700001500247700001900262700001200281700001400293700001200307245014600319856006700465300000800532490000700540520182300547022001402370       2022                            d  bMDPI AG10aElectrical and Electronic Engineering10aMechanical Engineering10aControl and Systems Engineering1 aOyibo P1 aJujjavarapu S1 aMeulah B1 aAgbana T1 aBraakman I1 avan Diepen A1 aBengtson M1 aVan Lieshout L1 aOyibo W1 aVdovine G1 aDiehl J00aSchistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings  uhttps://www.mdpi.com/2072-666X/13/5/643/pdf?version=1650354504  a6430 v133 a<p>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.</p>
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