Automated detection and staging of malaria parasites from cytological smears using convolutional neural networks Article Swipe
Mira S. Davidson
,
Clare Andradi-Brown
,
Sabrina Yahiya
,
Jill Chmielewski
,
Aidan J. O’Donnell
,
Pratima Gurung
,
Myriam D. Jeninga
,
Parichat Prommana
,
Dean Andrew
,
Michaela Petter
,
Chairat Uthaipibull
,
Michelle J. Boyle
,
George W. Ashdown
,
Jeffrey D. Dvorin
,
Sarah E. Reece
,
Danny W. Wilson
,
Kane A. Cunningham
,
D. Michael Ando
,
Michelle Dimon
,
Jake Baum
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1017/s2633903x21000015
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1017/s2633903x21000015
Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.
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- Type
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- en
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- https://doi.org/10.1017/s2633903x21000015
- https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8573173B4952D45CA7618E548977EB50/S2633903X21000015a.pdf/div-class-title-automated-detection-and-staging-of-malaria-parasites-from-cytological-smears-using-convolutional-neural-networks-div.pdf
- OA Status
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- Cited By
- 42
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3203147350
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3203147350Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1017/s2633903x21000015Digital Object Identifier
- Title
-
Automated detection and staging of malaria parasites from cytological smears using convolutional neural networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Mira S. Davidson, Clare Andradi-Brown, Sabrina Yahiya, Jill Chmielewski, Aidan J. O’Donnell, Pratima Gurung, Myriam D. Jeninga, Parichat Prommana, Dean Andrew, Michaela Petter, Chairat Uthaipibull, Michelle J. Boyle, George W. Ashdown, Jeffrey D. Dvorin, Sarah E. Reece, Danny W. Wilson, Kane A. Cunningham, D. Michael Ando, Michelle Dimon, Jake BaumList of authors in order
- Landing page
-
https://doi.org/10.1017/s2633903x21000015Publisher landing page
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https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8573173B4952D45CA7618E548977EB50/S2633903X21000015a.pdf/div-class-title-automated-detection-and-staging-of-malaria-parasites-from-cytological-smears-using-convolutional-neural-networks-div.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://www.cambridge.org/core/services/aop-cambridge-core/content/view/8573173B4952D45CA7618E548977EB50/S2633903X21000015a.pdf/div-class-title-automated-detection-and-staging-of-malaria-parasites-from-cytological-smears-using-convolutional-neural-networks-div.pdfDirect OA link when available
- Concepts
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Computer science, Convolutional neural network, Artificial intelligence, Blood smear, Pattern recognition (psychology), Standardization, Gold standard (test), Artificial neural network, Quality assurance, Malaria, Pathology, External quality assessment, Radiology, Medicine, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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42Total citation count in OpenAlex
- Citations by year (recent)
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2025: 9, 2024: 16, 2023: 9, 2022: 8Per-year citation counts (last 5 years)
- References (count)
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45Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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