Automated Recognition of Plasmodium falciparum Parasites from Portable Blood Levitation Imaging. Issue 28 (11th August 2022)

• Record Type: Journal Article
• Authors: Deshmukh, Shreya S.; Byaruhanga, Oswald; Tumwebaze, Patrick; Akin, Demir; Greenhouse, Bryan; Egan, Elizabeth S.; Demirci, Utkan
• Is Part Of: Advanced science. Volume 9:Issue 28(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-11
• Subjects: computer vision -- malaria -- portable imaging -- resource‐limited settings; Science -- Periodicals; 505
• DOI: 10.1002/advs.202105396
• Languages: English
• ISSNs: 2198-3844

Abstract

Abstract: 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. Abstract : This study presents an automated phenotypic approach to detecting malaria parasites in field settings. Red blood cells are lysed then suspended in a magnetic field, to be imaged on a portable imaging platform. Morphological and texture features are extracted from this image data and used to train a classifier with 95% accuracy on patient samples tested in eastern Uganda.