Wednesday, October 4, 2023

Performance evaluation of the AiDx multi-diagnostic automated microscope for the detection of schistosomiasis in Abuja, Nigeria – Scientific Reports

Schistosomiasis is a chronic parasitic disease that is caused by trematode blood flukes of the genus Schistosoma, with water snails serving as the intermediate host. Transmission has been reported from 78 countries, the majority of which are classified as low- or middle-income countries, according to (WHO) report and more than 700 million people are at risk of infection1. There are 2 major forms of schistosomiasis—intestinal (due to Schistosoma mansoni and S. japonicum) and urogenital (predominantly due to S. haematobium). Common signs and symptoms of urogenital Schistosoma haematobium include a swollen belly, blood in the urine, stunted growth, cognitive impairment in children and infertility among adults of childbearing age. Advanced disease may sometimes present with fibrosis of the bladder and ureter, kidney damage and bladder cancer. Diagnosis is normally done through the detection of parasite eggs in urine or stool specimens using microscopy2,3.

Manual microscopy is the World Health Organization reference diagnostic method for the diagnosis of S. haematobium infection. Parasite-derived products (including eggs) in stool and urine can be identified and the eggs quantified. The egg count indicates the intensity of infection in a patient and can also reflect exposure, or prevalence in the target population. It is therefore important for therapeutic monitoring and surveillance3,4,5. However, manual microscopy is laborious, expertise-dependent and requires basic laboratory and power infrastructure, limiting its point-of-care application in endemic rural areas. Furthermore, the ratio of trained personnel to workload in endemic settings is low and this often results in a high workload per technician6,7. Innovative approaches to diagnosis that are easy-to-use and suitable for endemic resource-limited settings are required to diagnose infections and complement control and elimination efforts. Digital optical diagnostic devices with integrated Artificial Intelligence (AI) have been developed for the detection of S. haematobium eggs, soil transmitted helminths and other Neglected Tropical Diseases8,9,10,11,12,13,14,15,16,17. However, recent review of optical diagnostic devices reports that none of the developed devices had attained a technological readiness level required for use at point of care9.

Antigen-based diagnostic alternatives to conventional microscopy developed for schistosomiasis diagnosis include Point-Of-Care tests detecting Circulating Cathodic Antigens (POC-CCA) and the Up-Converting Particle Lateral Flow assay developed to detect Circulating Anodic Antigens (UCP-LF CAA) in either serum or urine18,19. These diagnostic techniques however require a level of infrastructure and training higher than microscopy and are non-specific in settings with S. haematobium only and on pregnant women. They also require standardization as a result of batch-to-batch variation. The use of Nucleic Acid Amplification Tests (NAATs) has been confined mainly to research settings as it is expensive and impractical at point of care20,21,22. Therefore, conventional microscopy remains the most widely used method.

In this work we assessed the performance of the novel multi-diagnostic AiDx Assist automated microscope for the quick detection of Schistosoma haematobium. The AI integrated innovative device was evaluated using urine samples to detect S. haematobium eggs in urine.

The Ethical Approval was received from the Federal Capital Territory (FCT, Nigeria) Health Research Ethics Committee (FCT, HREC) under approval number: FHREC/2022/01/102/05-07-22 and all research was performed in accordance with the relevant guidelines and regulations. The project was introduced in detail to the NTD Unit, Public Health Department, Federal Capital Territory Abuja (FCTA) who subsequently communicated details of the project to the local NTD officer in the selected area councils.

Description of the AiDx assist microscope

The multi-diagnostic AiDx Assist Microscope device shown in Fig. 1, is a low-cost and compact automated single-slide scanner and microscope. The optical train consists of a 4× microscope objective (numerical aperture NA of 0.10 and a working distance of 18.0 mm). The current system model is designed with a Sony IMX 178 CMOS sensor 6.41 Mpix (3088 × 2076 pixel) with a pixel size of 2.40 μm.

Figure 1

AiDx Assist fully automated microscopes designed for laboratory and field use.

The computer vision pipeline in this version of the device is powered by the U-Net segmentation model with a ResNet-18 network architecture. The approximate schistosome ova count was estimated using the predicted segmentation mask23,24. More recent approaches are being explored to improve the performance of the integrated algorithm.

The AiDx Assist multi-diagnostic device can detect microfilaria, S. haematobium and S. mansoni eggs in blood, urine, and stool samples respectively. Furthermore, the device has shown promising potential in the detection of malaria parasites and tuberculosis in prepared blood and sputum samples accordingly.

The AiDx Assist multi-diagnostic device can detect microfilaria, S. haematobium and S. mansoni eggs in blood, urine, and stool samples respectively.

The AiDx Assist Microscope can function in two modes at the choice of the operator:

Semi-automated mode

In the semi-automated mode, the automatic parasite count algorithm is disabled. The device operates without the use of the integrated Artificial Intelligence software. Images registered and stored in the system are visually examined for the presence of the target parasite. Operating the device in this mode is very useful for experienced laboratory scientists who prefer to gain control of the device and navigate through the stored sample scan to detect the target of their interest without dependence on the AI. The integrated sample autofocusing mechanism and scanning system control algorithm reduces the time, error, fatigue, and other limitations associated with manual microscopy.

Fully automated mode

The fully automated AiDx Assist include an autofocusing algorithm which obtains an in-focus image obtained in a z-stack of image, a sample scanning algorithm to scan defined samples area. It also includes image registration, processing, AI image analysis and automatic parasite count. Detected targets are segmented and segmented images are stored in a folder for easy access and validation by the AiDx system operator. The AI Assist software outputs the number of estimated ova for the operator to validate and confirm the presence of the segmented Schistosoma ova.

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