Frontiers in parasitology · 2026
A multi-site laboratory evaluation of the MEDSCAN application for automated POC-CCA interpretation.
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Scherr Thomas F, Hoekstra Pytsje T, Abdoel Theresia, Moore Carson P, Hardcastle Austin, Boisse Matthew, Mulonga Boaz, Andiego Kennedy, Ogara George, Odhiambo Gladys, Rawago Fredrick, Spurlock Nicholas, Howard Alexandrea A, Pask Megan E, van Dam Eline M, van Dam Govert J, Ho Chris, Chami Goylette F, Haselton Frederick, Odiere Maurice R, Wright David
Аннотация
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.
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.