Evaluation of the AiDx Assist device for automated detection of Schistosoma eggs in stool and urine samples in Nigeria
Brice Meulah, Pytsje T. Hoekstra, Samuel Popoola, Satyajith Jujjavarapu, Moses Oluwaseun Aderogba, Joseph Olusesan Fadare et al. · 2025 · 4 citationsRead the paper
Introduction Schistosomiasis is a public health concern and there is a need for reliable field-compatible diagnostic methods in endemic settings. The AiDx Assist, an artificial intelligence (AI)-based automated microscope, has shown promising results for the detection of Schistosoma haematobium eggs in urine. It has been further developed to detect Schistosoma mansoni eggs in stool. Methods In this study, we evaluated the performance of the AiDx Assist for the detection of S. mansoni eggs in stool samples and further validated the performance of the AiDx Assist for the detection of S. haematobium eggs in urine samples. Additionally, the potential of the AiDx Assist for the detection of other helminths in stool samples was explored. In total, 405 participants from an area endemic for both S. mansoni and S. haematobium provided stool and urine samples which were subjected to AiDx Assist (semi- and fully automated), while conventional microscopy was used as the diagnostic reference. Results Only samples with complete test results were included in the final analysis, resulting in 375 stool and 398 urine samples, of which 38.4% and 65.3% showed Schistosoma eggs by conventional microscopy. The collected images of the stool samples were retrospectively examined for other helminth eggs via manual analysis. For the detection of S. mansoni eggs, the sensitivity of the semi-automated AiDx Assist (86.8%) was significantly higher compared to the fully automated AiDx Assist (56.9%) while the specificity was comparable, with 81.4% and 86.8%, respectively. Retrospectively, eggs of Ascaris lumbricoides and Trichuris trichiura were visualized. For the examination of urine samples, a comparable sensitivity in the detection of S. haematobium eggs was found between the semi-and the fully automated modes of the AiDx Assist, showing 94.6% and 91.9%, respectively. Furthermore, the specificity was comparable, with 90.6%and 91.3% respectively. Discussion The AiDx Assist met the World Health Organization Target Product Profile criteria in terms of diagnostic accuracy for the detection of S. haematobium eggs in urine samples and performed modestly in the detection of S. mansoni eggs in stool samples. With some further improvements, it has the potential to become a valuable diagnostic tool for screening multiple helminth parasites in stool and urine samples.
What the full text shows
Tested in real conditions- Semi-automated AiDx Assist showed higher sensitivity for S. mansoni eggs in stool than the fully automated mode. — 86.8% vs 56.9% sensitivity (Stool samples on Kato-Katz slides)
- Fully automated AiDx Assist showed comparable specificity to semi-automated mode for S. mansoni eggs. — 86.8% vs 81.4% specificity (Stool samples on Kato-Katz slides)
- AiDx Assist showed high and comparable sensitivity for S. haematobium eggs in urine across both modes. — 94.6% (semi-automated) and 91.9% (fully automated) sensitivity (Urine samples via urine filtration)
- AiDx Assist showed high and comparable specificity for S. haematobium eggs in urine across both modes. — 90.6% (semi-automated) and 91.3% (fully automated) specificity (Urine samples via urine filtration)
- The device demonstrated multi-diagnostic potential by visualizing other helminth eggs. — Detected Ascaris lumbricoides and Trichuris trichiura (Retrospective manual analysis of stool images)
- Setup
- 405 participants (aged 5-14) from five endemic communities in Abuja, Nigeria; final analysis included 375 stool and 398 urine samples.
- How
- The study compared a semi-automated mode (expert visual review of digital images) and a fully automated mode (AI-driven detection) of the AiDx Assist device against conventional microscopy. Stool samples were processed via Kato-Katz slides and urine samples via polycarbonate membrane filtration.
- Limitations
- Fully automated AI algorithm for S. mansoni struggles with organic matter and artifacts in stool samples. · AI algorithm missed more than half of the light infections (1-99 EPG). · Kato-Katz smear variability due to stool properties and stain preparation.
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