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

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Diagnostic Tests, Routine/*instrumentation ; Diagnostic Tests, Routine/*methods ; Malaria, Falciparum/*diagnosis; Adult ; Azure Stains ; Blood Specimen Collection/methods ; Deep Learning ; Fluorescence ; Humans ; Malaria/parasitology ; Malaria, Falciparum/parasitology ; Microscopy, Fluorescence ; Parasitemia/diagnosis ; Plasmodium/parasitology ; Plasmodium falciparum/pathogenicity ; Point-of-Care Testing
    • Abstract:
      Background: 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.
      Methods: 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.
      Results: 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.
      Conclusion: 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.
      Competing Interests: Johan Lundin and Mikael Lundin are founders and co-owners of Aiforia Technologies Oy, Helsinki, Finland. The Nvidia Corporation donated a GPU via the Academic Grant Application Program, which was used during this study. The prototype instrument used for digitization of the samples is developed and patented (patent number: US20180246306) by the University of Helsinki (Helsinki, Finland). This does not alter our adherence to PLOS ONE policies on sharing data and materials.
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    • Accession Number:
      0 (Azure Stains)
    • Publication Date:
      Date Created: 20201117 Date Completed: 20210104 Latest Revision: 20210104
    • Publication Date:
      20240105
    • Accession Number:
      PMC7671488
    • Accession Number:
      10.1371/journal.pone.0242355
    • Accession Number:
      33201905