ArticleFrontiers in parasitology2026
A multi-site laboratory evaluation of the MEDSCAN application for automated POC-CCA interpretation.
Article in Frontiers in parasitology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.Nature communications · 2026Article
- Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
21 authors.
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Abstract
Introduction: Control efforts against schistosomiasis are hampered by the subjective interpretation of the point-of-care circulating cathodic antigen (POC-CCA) urine test, which limits diagnostic consistency. We developed MEDSCAN (Mobile-Enabled Diagnostics for Schistosomiasis Control Analytics), a mobile application that uses smartphone imaging and computer vision to automate POC-CCA interpretation. Methods: In a multi-site laboratory evaluation across the USA, the Netherlands, and Kenya, we compared MEDSCAN to visual G-Score interpretation and a benchtop lateral flow reader (LFR). Results: All three methods produced clear concentration-response relationships, with normalized machine-based metrics achieving AUROC ≥ 0.90. MEDSCAN demonstrated excellent inter-user reproducibility (intra-class correlation coefficients exceeding 0.94) and substantial agreement with both visual and LFR interpretations across sites. Some device-to-device variability was observed, attributable to differences in smartphone camera hardware and image processing; however, binary diagnostic outcomes remained robust across a heterogeneous set of smartphones. Discussion: These results establish operational positivity thresholds for MEDSCAN-based on test-line signal alone or normalized metrics-suitable for direct implementation in field surveillance workflows. A large-scale field study is underway to evaluate MEDSCAN under routine POC-CCA surveillance conditions in endemic settings.
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