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

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.