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

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 learn...

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Bibliographic Details
Published in:PloS one 2020-11, Vol.15 (11), p.e0242355-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: Article
Language:English
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0242355