Microscopy remains the gold standard for identification of malaria parasites. However, the sensitivity of malaria microscopy is low. This study evaluated the impact of repeated sampling up to 12 h in 177 children < 6 years with suspected malaria.
Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster segmentation, followed by a second stage Faster R-CNN for detecting small cell objects within clusters, identified as connected components from the U-Net stage.
The World Health Organization (WHO) recommends use of parasitological diagnosis of malaria for all age groups in all malaria transmission settings. Many private health facilities rely on malaria microscopy for malaria diagnosis. However, quality of malaria microscopy is affected by number of factors including availability of skilled laboratory microscopists and lack of quality assurance systems in many malaria endemic countries. This study was carried out to assess quality of malaria microscopy in selected private health facilities in Tanzania.
CareStart™ malaria HRP2/pLDH (Pf/pan) combo test is one of the several rapid diagnostic tests (RDT) approved for diagnosis of malaria at the point of care in Tanzania. However, there are limited studies on the diagnostic performance of RDT after wide scale use in primary health care facilities in Tanzania.