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

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Abstract

BackgroundUrogenital 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. MethodsWe 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 I{superscript 2} statistic. Data were analyzed using STATA version 14.1 and Review Manager version 5.4.1. ResultsA total of ten studies comprising 18 datasets and 7,318 participants met the eligibility criteria. The pooled sensitivity and specificity of AI-assisted diagnostic tools compared with standard microscopy were 0.88 (95% CI: 0.84-0.92) and 0.89 (95% CI: 0.85-0.93), respectively. The pooled diagnostic odds ratio (DOR) was 64 (95% CI: 38-106), and the area under the summary receiver operating characteristic curve (AUC) was 0.95 (95% CI: 0.92-0.96). The positive likelihood ratio (PLR) was 28.86 (95% CI: 12.01- 67.00), while the negative likelihood ratio (NLR) was 0.03 (95% CI: 0.01-0.10). Subgroup analysis revealed higher diagnostic accuracy of AI-assisted tools in community surveys (sensitivity = 0.93, specificity = 0.90) and superior pooled performance for AiDx platforms (sensitivity = 0.93, specificity = 0.91) compared with SchistoScope (sensitivity = 0.86, specificity = 0.86). Overall, substantial heterogeneity was observed across studies (I{superscript 2} = 99%). ConclusionAI-assisted diagnostic tools demonstrate high accuracy for detecting S. haematobium and could serve as valuable complements to existing diagnostic approaches. However, further field validation, standardization and integration into routine diagnostic workflows are needed to ensure their reliability and scalability in real-world settings.

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