Multi-contrast machine learning improves schistosomiasis diagnostic performance.

ABSTRACT: Schistosomiasis currently affects over 250 million people and remains a public health burden despite ongoing global control efforts. Conventional microscopy is a practical tool for diagnosis and screening of Schistosoma haematobium, but identification of eggs requires a skilled microscopist. Here we present a machine learning (ML)-based strategy for automated detection of S. haematobium that combines two imaging contrasts, brightfield (BF) and darkfield (DF), to improve diagnostic performance. We collected BF and DF images of urine samples, many of them containing S. haematobium eggs, during two different field studies in Côte d'Ivoire using a mobile phone-based microscope, the SchistoScope. We then trained separate egg-detection ML models and compared the patient-level performance of BF a

SUBMITTER: de Leon Derby MD 

PROVIDER: S-EPMC12334053 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature