In this research, we report on the performance of automated optical digital detection and quantification of Schistosoma haematobium provided by AiDx NTDx multi-diagnostic Assist microscope. Our study was community-based, and a convenient sampling method was used in 17 communities in Abuja Nigeria, based on the disease prevalence information extracted from the baseline database on schistosomiasis, NTD Division, of the Federal Ministry of Health. At baseline, samples from 869 participants were evaluated of which 358 (34.1%) tested S. haematobium positive by the reference diagnostic standard. Registered images from the fully automated (autofocusing, scanning, image registration and processing, AI image analysis and automatic parasite count) AiDx assist microscope were analyzed. The Semi automated (autofocusing, scanning, image registration & processing and manual parasite count) and the fully automated AiDx Assist showed comparable sensitivities and specificities of [90.3%, 98%] and [89%, 99%] respectively. Overall, estimated egg counts of the semi-automated & fully automated AiDx Assist correlated significantly with the egg counts of conventional microscopy (r = 0.93, p ≤ 0.001 and r = 0.89, p ≤ 0.001 respectively). The AiDx Assist device performance is consistent with requirement of the World Health Organization diagnostic target product profile for monitoring, evaluation, and surveillance of Schistosomiasis elimination Programs.

Schistosomiasis is a neglected tropical disease of Public Health importance affecting over 252 million people worldwide with Nigeria having a very high number of cases. It is caused by blood flukes of the genus Schistosoma and transmitted by freshwater snails. To achieve the current global elimination objectives, low-cost and easy-to-use diagnostic tools are critically needed. Recent innovations in optical and computer technologies have made handheld digital and smartphone-based microscopes a viable diagnostic approach. Development, validation and deployment of these diagnostic devices for field use, however, require the optimisation of its optical train for the registration of high-resolution images and the realisation of a robust system design that can be locally produced in low-income countries. Field research conducted in Nigeria with active involvement of key stakeholders in research and development (RD) led to the design of an initial prototype device for the diagnosis of urinary schistosomiasis, called Schistoscope 1.0. In this paper, we present further development of the Schistoscope 1.0 along two parallel design trajectories: A Raspberry Pi and a Smartphone-based Schistoscope. Specifically, we focused on the optimization of the optics, embodiment design and the electronics systems of the devices so as to produce a robust design with potential for local production.

Schistosomiasis is a treatable and preventable neglected tropical disease of Public Health importance affecting over 250 million people worldwide while Nigeria is one of the high burden countries. Currently available diagnoses are cumbersome, low in sensitivity and not field-adaptable given the high skill required that are not available in the rural settings where the diseases are majorly prevalent. Democratizing access to diagnosis with a rapid, easy-to-use, accurate diagnosis is critical in currently stepped-up control, pre-elimination and elimination strategies for urinary schistosomiasis. In this paper, we describe the design process of a low-cost smartphone-based microscope for rapid diagnosis of urinary Schistosomiasis. Field research conducted in Nigeria with the active involvement of key stakeholders in the research and development (RD) process validated our assumptions and enabled the development of our proof-of-concept into a working prototype in three iterative designs steps. Through this design process, we investigated the local development of technical optics for good quality imaging and explored the simplification of sample preparation techniques using commonly available materials. Starting from the first iteration, the output of each design step was used as the input to the subsequent iterations to optimize our system design. Insightful results and input from the field demonstrated that an adaptive design approach was needed to facilitate the rapid development and deployment of point-of-care diagnostic devices for use in low-resource settings. It is our goal that these devices will be locally manufactured in Nigeria to expand access to the test given her huge population and high disease burden, quick repairs, and easy maintenance on the field.