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

PLoS Comput Biol. 2021 Aug 9;17(8):e1009257. doi: 10.1371/journal.pcbi.1009257. eCollection 2021 Aug.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Computational Biology
  • Deep Learning
  • Diagnosis, Computer-Assisted
  • Erythrocytes / parasitology
  • Humans
  • Image Interpretation, Computer-Assisted
  • Malaria, Falciparum / diagnostic imaging
  • Malaria, Falciparum / parasitology*
  • Microscopy, Ultraviolet / instrumentation
  • Microscopy, Ultraviolet / methods
  • Neural Networks, Computer
  • Parasitemia / diagnosis*
  • Parasitemia / diagnostic imaging
  • Parasitemia / parasitology*
  • Plasmodium falciparum / classification*
  • Plasmodium falciparum / cytology*
  • Plasmodium falciparum / growth & development

Grants and funding

This work was funded by the Chan Zuckerberg Biohub. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. JD, RGS, PL, and VNPV earn salaries from Chan Zuckerberg Biohub.