Schistoscope: An Automated Microscope with Artificial Intelligence for Detection of Schistosoma haematobium Eggs in Resource-Limited Settings

Abstract

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.

Performance Evaluation of the Schistoscope 5.0 for (Semi-)automated Digital Detection and Quantification of Schistosoma haematobium Eggs in Urine: A Field-based Study in Nigeria

“…The Schistoscope 5.0 ( Figure 1 ) is a low-cost automated slide-scanner digital microscope that can be supported with AI algorithms for image processing. 18 The system is composed of custom-designed optical bright-field illumination, three-axis movement (X, Y, Z), and electronic and computing modules. The illumination module comprises a bright white light-emitting diode and condenser lenses to generate uniform illumination.…”

“…The on-board computer is a Raspberry Pi 4B connected to a Raspberry Pi HQ camera that has a pixel size of 1.55 µm and an image resolution of 2028 × 1520 pixels. The current study used a 4× microscope objective that provides an experimental resolution limit of 3.26 μm, 18 which is sufficient to resolve S. haematobium eggs ( Figure 1C ). The device runs on mains electricity and does not have a built-in battery.…”

“… 16 , 17 In a recent proof-of-principle study, the device and its AI algorithms were trained successfully with phosphate buffer saline and urine samples that were spiked with S. haematobium eggs obtained from a laboratory maintained parasite life cycle and a limited number of clinical samples. 18 This led to the conclusion that the Schistoscope was ready for further validation. The aim of the current study is to evaluate the performance of the Schistoscope 5.0 as a semi- and fully automated digital microscope for the detection and quantification of S. haematobium eggs in a prospective study design under field conditions.…”

The development of an artificial intelligence-based digital pathology for neglected tropical diseases: A platform specific analysis of the World Health Organization diagnostic target product profile for soil-transmitted helminthiasis

“…Glycerine or mounting medium may be added to the egg containing filter membranes on a glass slide, allowing later examination. Hence this equipment should not be limited to KK for STH and S. mansoni but should include filtration technique for S. haematobium too (54,55). The capability of the AI component introduces the multiplexing capacity within the same sample for each determinant that it has been trained with.…”

A review on innovative optical devices for the diagnosis of human soil-transmitted helminthiasis and schistosomiasis: from research and development to commercialization

“…Of the 8 non-smartphone-based optical devices reviewed here, six were limited to proof-of-concept (Linder et al, 2013;Holmstrom et al, 2017;Sukas et al, 2019;Agbana et al, 2020;Dacal et al, 2021;Oyibo et al, 2022) as reported by the authors. The first study by Linder et al demonstrated the use of a mini-microscope (On-Chip) to image and detect the eggs of S. haematobium with AI (Linder et al, 2013).…”