Accuracy of AI-assisted diagnostic tools for Schistosoma haematobium: A systematic review and meta-analysis

Sisay Desale, Getaneh Alemu and Tadesse Hailu

PLOS Neglected Tropical Diseases, 2026, vol. 20, issue 5, 1-19

Abstract: Background: Urogenital schistosomiasis caused by Schistosoma haematobium remains endemic in sub-Saharan Africa. Diagnosis traditionally relies on urine microscopy to detect parasite eggs; however, its sensitivity declines in low-intensity infections. Artificial intelligence (AI)-assisted image analysis offers a promising approach to automate egg detection and enhance diagnostic accuracy, but its performance compared with standard microscopy is not well established. Methods: We conducted a systematic review following the PRISMA guidelines and checklist. Studies evaluating AI-assisted detection of S. haematobium compared with microscopy and/or molecular reference standards, published up to August 2025, were identified through searches in PubMed/MEDLINE, HINARI, Epistemonikos, Science Direct, Google Scholar and grey literature sources. Eligible studies were selected based on pre-defined inclusion and exclusion criteria. The quality of included studies was assessed using the QUADAS-2 tool. Heterogeneity among studies was evaluated using the Cochrane Q test and I² statistic. Data was analyzed using STATA version 14.1 and Review Manager version 5.4.1. Results: Ten studies (15 datasets, 5,564 urine samples) conducted in sub-Saharan Africa met the inclusion criteria. AI-assisted tools demonstrated high diagnostic accuracy. The pooled sensitivity was 88% (95%CI 83%-91%) and pooled specificity was 89% (95% CI 83%-93%). The pooled diagnostic odds ratio was 54.00 (95% CI 30.41-95.88), indicating strong discrimination between infected and uninfected cases. The SROC curve yielded an AUC of 0.94 (95% CI 0.92-0.96), reflecting excellent overall accuracy. Heterogeneity across studies was high (I² = 100%), suggesting results varied by the specific AI platform and study context. Conclusion: AI-assisted microscopic diagnosis of S. haematobium achieved very good in this meta-analysis. These automated tools, whether smartphone-based or bench-top systems, showed promise for detecting infections and could help screen populations in endemic areas. With further validation in field settings and comparison to highly sensitive reference tests, AI diagnostic technology may become a valuable tool to improve case detection and support schistosomiasis control and elimination efforts. Author summary: Schistosomiasis (often called bilharzia) is a disease caused by a type of blood fluke worm that lives in freshwater snails. It affects millions of people, especially in poor tropical regions. Schistosoma haematobium is the species that causes urinary schistosomiasis. Infected people often suffer from blood in the urine, bladder problems, and kidney damage. Controlling schistosomiasis means finding and treating infected people. However, the usual way to diagnose it is by looking for parasite eggs under a microscope that can miss many infections, particularly when the number of eggs is low or lab facilities are limited. This makes it hard to reach goals for elimination of the disease. Artificial intelligence (AI) offers a new solution. Scientists have developed devices like smartphone microscopes or automated scanners that use AI programs to recognize parasite eggs in urine pictures. We collected and analyzed all published studies on these AI tools for detecting S. haematobium. We found that AI-based diagnostics are very accurate: on average they correctly identified 88% of infected cases and correctly ruled out about 89% of uninfected cases. In simpler terms, AI tools got nearly 9 out of 10 results right. Importantly, these tools work quickly and do not require highly trained experts. Our findings suggest that AI-assisted diagnosis could be a game-changer in low-resource settings. By making it easier and faster to screen for schistosomiasis, these technologies could help health workers find more infections that would otherwise be missed. Better detection means more people get treated, which lowers the spread of disease. In the long run, using AI tools for schistosomiasis could support global efforts to control and even eliminate this neglected tropical disease.

Date: 2026
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DOI: 10.1371/journal.pntd.0013703

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