Holmström O, Stenman S, Suutala A, Moilanen H, Kücükel H, Ngasala B, et al. (2020) A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy. PLoS ONE 15(11): e0242355. https://doi.org/10.1371/journal.pone.0242355
A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy
|Author:||Holmström, Oscar1; Stenman, Sebastian1; Suutala, Antti1;|
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
2Center of Microscopy and Nanotechnology, University of Oulu, Oulu, Finland
3Department of Women’s and Children’s Health, International Maternal and Child Health (IMCH), Uppsala University, Uppsala, Sweden
4Department of Medical Entomology and Parasitology, School of Public Health, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
5Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden
6Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
|Online Access:||PDF Full Text (PDF, 1.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202102154858
Public Library of Science,
|Publish Date:|| 2021-02-15
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.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
217 Medical engineering
This work was funded by the Swedish Research Council, Swedish International Development Agency (SIDA) and Sigrid Jusélius Foundation. In addition, the study has been supported by Finska Läkaresällskapet, Biomedicum Foundation, Medicinska Understödsföreningen Liv och Hälsa rf, the Nvidia Corporation and Wilhelm och Else Stockmanns stiftelse. We furthermore greatly acknowledge the assistance and support from the Helsinki Institute of Life Science (HiLIFE) and the FIMM Digital Microscopy and Molecular Pathology Unit supported by Helsinki University and Biocenter Finland. The funders did not play any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2020 Holmström et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.