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Label-free imaging and classification of live P. falciparum enables high performance parasitemia quantification without fixation or staining.

ABSTRACT: Manual microscopic inspection of fixed and stained blood smears has remained the gold standard for Plasmodium parasitemia analysis for over a century. Unfortunately, smear preparation consumes time and reagents, while manual microscopy is skill-dependent and labor-intensive. Here, we demonstrate that deep learning enables both life stage classification and accurate parasitemia quantification of ordinary brightfield microscopy images of live, unstained red blood cells. We tested our method using both a standard light microscope equipped with visible and near-ultraviolet (UV) illumination, and a custom-built microscope employing deep-UV illumination. While using deep-UV light achieved an overall four-category classification of Plasmodium falciparum blood stages of greater than 99% and a recall of 89.8% for ring-stage parasites, imaging with near-UV light on a standard microscope resulted in 96.8% overall accuracy and over 90% recall for ring-stage parasites. Both imaging systems were tested extrinsically by parasitemia titration, revealing superior performance over manually-scored Giemsa-stained smears, and a limit of detection below 0.1%. Our results establish that label-free parasitemia analysis of live cells is possible in a biomedical laboratory setting without the need for complex optical instrumentation. We anticipate future extensions of this work could enable label-free clinical diagnostic measurements, one day eliminating the need for conventional blood smear analysis.

SUBMITTER: Lebel P

PROVIDER: S-EPMC8376094 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Label-free imaging and classification of live P. falciparum: raw UV dataset

Project description: Not available

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Label-free imaging and classification of live P. falciparum: raw Leica dataset

Project description: Not available

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Label-free imaging and classification of live P. falciparum: processed UV dataset

Project description: Not available

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Label-free imaging and classification of live P. falciparum: processed Leica dataset

Project description: Not available

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VAR2CSA signatures of high Plasmodium falciparum parasitemia in the placenta.

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