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Abstract Background Accurate diagnostic tools are needed in schistosomiasis elimination settings to determine prevalence thresholds for assigning or stopping interventions, guide pre- and post-elimination surveillance, and verify whether elimination has been reached. We assessed the accuracy of six different diagnostic tests in Pemba, Tanzania, a setting approaching Schistosoma haematobium elimination. Methodology A prospective diagnostic accuracy study was conducted from February to April 2025. From an initial cross-sectional single-day urine filtration (UF)-microscopy screening of 784 students, 69 S. haematobium -positive and 212 negative students were randomly selected for longitudinal sample collection. Four additional urine samples collected over four different days, were available from 262/281 participants and analysed by UF-microscopy. One sample per participant was analysed in parallel with five additional diagnostics: microscopy-based artificial intelligence (AI)-scanner, Schistosoma -ITS-2 qPCR, S. haematobium -Dra-1 recombinase polymerase amplification (RPA), Hemastix reagent strips, and up-converting particle lateral flow circulating anodic antigen assay (UCP-LF CAA). We assessed the sensitivity and specificity of the different diagnostics, using 5-day UF-microscopy examinations as reference test. Principal Findings A total of 85/262 participants were S. haematobium -positive using 5-day UF-microscopy. Directly compared with the reference test, the sensitivity for single-sample examination was: AI-scanner: 76.7% (95% confidence interval (CI): 71.0-82.5%); qPCR: 76.0% (95% CI: 70.1-81.4%); UF-microscopy: 61.2% (95% CI: 55.3-67.1%); RPA: 56.1% (95% CI: 50.0-62.2%); Hemastix: 44.6% (95% CI: 38.5-50.7%); and UCP-LF CAA: 30.6% (95% CI: 24.9-36.3%). Sensitivity increased with increasing infection intensity. The specificity of all investigated diagnostics was >92%, except for qPCR and RPA. Conclusion In near-elimination settings, multiple-day urine examination with standard UF-microscopy substantially improves case detection but is operationally challenging. For single-sample testing, among the six diagnostics investigated, the AI-scanner proved to be the most accurate. Hence, the AI-scanner might offer a promising alternative for research, clinical and programme use, but requires further validation in other settings and cost-effectiveness analyses. Trial registration clinicaltrials.gov, NCT06808750 . Registered 08 January 2025, https://clinicaltrials.gov/study/NCT06808750 . Author Summary As countries progress towards the schistosomiasis elimination goals set by the World Health Organization for 2030, case numbers and infection intensities decrease, which brings about diagnostic challenges. Indeed, accurate diagnostic tools are needed to precisely determine where to assign or stop interventions, implement test-and-treat approaches, and enable verification of elimination plus surveillance. We conducted the first prospective, head-to-head accuracy evaluation of six diagnostic tests, ranging from standard urine filtration (UF-) microscopy and haematuria assessment to advanced molecular and antigen tests and a new artificial intelligence (AI-) scanner for egg microscopy, all evaluated using the same urine sample, for S. haematobium detection in a near-elimination setting. We showed that examining multiple-day urine samples from the same individual with standard UF-microscopy, substantially increased the number of cases, especially when infection intensities were light. With 5-day UF-microscopy as the reference test, the specificity of all investigated single-sample tests was >92%, except for qPCR and S. haematobium -Dra-1 recombinase polymerase amplification. Sensitivity was >60% for the novel AI-scanner, qPCR and UF-microscopy. Sensitivity of all tests increased with increasing infection intensity. Schistosomiasis control and elimination programmes relying on single-sample UF-microscopy risk missing a substantial proportion of infections, impeding intervention decisions and jeopardising elimination targets. Multiple-day UF-microscopy improves case detection but is operationally challenging. For single-sample testing in near-elimination settings, an AI-scanner offers a promising alternative for research, clinical and programme use, warranting further cost-effectiveness and implementation studies.