Synthetic Data for Equitable Artificial Intelligence in Rapid Diagnostic Test Interpretation

Rapid diagnostic tests (RDTs) support affordable disease diagnosis. Machine learning (ML) can improve RDT interpretation but often relies on large, proprietary, and costly real-world image libraries. We present SynSight – a ML-enabled RDT segmentation and classification pipeline trained on synthetic data. Validated on HIV (98% sensitivity, 99% specificity) and COVID-19 RDTs (up to 99% accuracy), SynSight enables rapid ML training without real-world images, keeping pace with new RDT development.