Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.

ABSTRACT: There is a lack of automated pipelines for diagnostic classification of point-of-care tests for neglected tropical diseases. Here we present an end-to-end automated pipeline for the analysis of point-of-care circulating cathodic antigen tests for schistosomiasis. We incorporated deep learning for cassette segmentation with signal processing. Automated classifications were compared to quantitative readings from calibrated antigen samples examined in lateral flow readers, and visual readings from highly trained field and senior technicians. The pipeline was evaluated for 3188 individuals within the SchistoTrack cohort in rural Uganda. Our quantitative classifications were on par with a lateral flow reader, and showed 86.6% sensitivity and 96.5% specificity with visual readings from a senior

SUBMITTER: Ho C 

PROVIDER: S-EPMC12622112 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature