Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging.
Shreya S DeshmukhOswald ByaruhangaPatrick TumwebazeDemir AkinBryan GreenhouseElizabeth S EganZhonglin LyuPublished in: Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2022)
In many malaria-endemic regions, current detection tools are inadequate in diagnostic accuracy and accessibility. To meet the need for direct, phenotypic, and automated malaria parasite detection in field settings, a portable platform to process, image, and analyze whole blood to detect Plasmodium falciparum parasites, is developed. The liberated parasites from lysed red blood cells suspended in a magnetic field are accurately detected using this cellphone-interfaced, battery-operated imaging platform. A validation study is conducted at Ugandan clinics, processing 45 malaria-negative and 36 malaria-positive clinical samples without external infrastructure. Texture and morphology features are extracted from the sample images, and a random forest classifier is trained to assess infection status, achieving 100% sensitivity and 91% specificity against gold-standard measurements (microscopy and polymerase chain reaction), and limit of detection of 31 parasites per µL. This rapid and user-friendly platform enables portable parasite detection and can support malaria diagnostics, surveillance, and research in resource-constrained environments.
Keyphrases
- plasmodium falciparum
- loop mediated isothermal amplification
- high throughput
- deep learning
- label free
- high resolution
- real time pcr
- machine learning
- red blood cell
- primary care
- public health
- climate change
- low cost
- optical coherence tomography
- computed tomography
- single molecule
- convolutional neural network
- fluorescence imaging
- silver nanoparticles