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Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears

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dc.contributor.authorAKÇAKIR, OSMAN
dc.contributor.authorCelebi, Lutfi Kadir
dc.contributor.authorKAMIL, MOHD
dc.contributor.authorALY, Ahmed Sayed Ibrahım
dc.contributor.institutionauthorAKÇAKIR, OSMAN
dc.contributor.institutionauthorKAMIL, MOHD
dc.contributor.institutionauthorALY, AHMED SAYED IBRAHıM
dc.date.accessioned2022-08-20T17:10:10Z
dc.date.available2022-08-20T17:10:10Z
dc.date.issued2022-07-01T00:00:00Z
dc.description.abstractDiagnosis of malaria in endemic areas is hampered by the lack of a rapid, stain-free and sensitive method to directly identify parasites in peripheral blood. Herein, we report the use of Fourier ptychography to generate wide-field high-resolution quantitative phase images of erythrocytes infected with malaria parasites, from a whole blood sample. We are able to image thousands of erythrocytes (red blood cells) in a single field of view and make a determination of infection status of the quantitative phase image of each segmented cell based on machine learning (random forest) and deep learning (VGG16) models. Our random forest model makes use of morphology and texture based features of the quantitative phase images. In order to label the quantitative images of the cells as either infected or uninfected before training the models, we make use of a Plasmodium berghei strain expressing GFP (green fluorescent protein) in all life cycle stages. By overlaying the fluorescence image with the quantitative phase image we could identify the infected subpopulation of erythrocytes for labelling purposes. Our machine learning model (random forest) achieved 91% specificity and 72% sensitivity while our deep learning model (VGG16) achieved 98% specificity and 57% sensitivity. These results highlight the potential for quantitative phase imaging coupled with artificial intelligence to develop an easy to use platform for the rapid and sensitive diagnosis of malaria.
dc.identifier.citationAKÇAKIR O., Celebi L. K. , KAMIL M., ALY A. S. I. , -Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears-, BIOMEDICAL OPTICS EXPRESS, cilt.13, sa.7, ss.3904-3921, 2022
dc.identifier.doi10.1364/boe.448099
dc.identifier.pubmed35991917
dc.identifier.scopus85132538002
dc.identifier.urihttp://hdl.handle.net/20.500.12645/30887
dc.identifier.wosWOS:000827275100002
dc.titleAutomated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears
dc.typeArticle
dspace.entity.typePublication
local.avesis.id7b08bfac-b469-48f8-b6b5-281cf26e2b5b
local.publication.isinternational1
relation.isAuthorOfPublication6a4af385-9fac-4013-8384-0beb290010a1
relation.isAuthorOfPublication90dbf8d7-063b-4fdc-9373-f3678809a1ea
relation.isAuthorOfPublication47a38002-6a0b-4c36-b83d-773a943d999d
relation.isAuthorOfPublication.latestForDiscovery90dbf8d7-063b-4fdc-9373-f3678809a1ea
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