A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy

• Published in: PloS one Vol. 15; no. 11; p. e0242355
• Main Authors: Holmström, Oscar, Stenman, Sebastian, Suutala, Antti, Moilanen, Hannu, Kücükel, Hakan, Ngasala, Billy, Mårtensson, Andreas, Mhamilawa, Lwidiko, Aydin-Schmidt, Berit, Lundin, Mikael, Diwan, Vinod, Linder, Nina, Lundin, Johan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 17.11.2020; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Artemisinin; Asexuality; Azure Stains; Biology and Life Sciences; Blood; Blood Specimen Collection - methods; Children & youth; Childrens health; Computer aided medical diagnosis; Correlation; Correlation analysis; Deep Learning; Diagnosis; Diagnostic systems; Diagnostic Tests, Routine - instrumentation; Diagnostic Tests, Routine - methods; Digital imaging; Digitization; Disease; Entomology; Fluorescence; Fluorescence microscopy; Global health; Humans; Infections; Infectious diseases; Machine learning; Malaria; Malaria - parasitology; Malaria, Falciparum - diagnosis; Malaria, Falciparum - parasitology; Maternal & child health; Medical diagnosis; Medicin och hälsovetenskap; Medicine; Medicine and Health Sciences; Methods; Microscopy; Microscopy, Fluorescence; Parasitemia - diagnosis; Parasites; Parasitology; Plasmodium - parasitology; Plasmodium falciparum; Plasmodium falciparum - pathogenicity; Point-of-Care Testing; Public health; Research and Analysis Methods; Vector-borne diseases; Visual observation; Visual signals; Womens health; Tanzania; Sweden; Finland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0242355

Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites. Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears. Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples. Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.