# Computer Vision for Malaria Microscopy: Automated Detection and Classification of Plasmodium for Basic Science and Pre-Clinical Applications

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2024 · $196,745

## Abstract

PROJECT SUMMARY/ABSTRACT
Among the “big three” infectious diseases worldwide, malaria stands out for the complexity of the Plasmodium
life-cycle and biology. Malaria parasites breed mainly within red blood cells, and across their lifespan there are
dramatic shifts in protein expression and metabolism that alter their appearance, behavior, and susceptibility to
clearance by the host immune system or antimalarial drugs. Because it is an infection of the blood, a biopsy
can be taken with a simple finger prick, and the ability to derive histopathological information via light
microscopy is a critical tool in the study of, and ultimately control and treatment of, malaria. Manual review is
painstaking and imperfect. Neural network-based computer vision (CV) approaches can accelerate data
acquisition from light microscopy and innovate new methods of extracting data currently only possible through
costly, labor-intensive benchtop molecular methods or time-consuming review by a small number of malaria
microscopy experts with the necessary training and experience to distinguish subtle differences between
parasite forms.
 This R21 proposal builds on 12 months of preparatory work supported by a pilot grant from The Johns
Hopkins University Institute for Data Intensive Engineering and Science, a collaborative pursuit of the Schools
of Medicine and Engineering. The co-principal investigators developed a deep learning-based CV algorithm
trained on a public dataset of >10,000 images of Plasmodium falciparum ring stage parasites that can detect
and quantify parasites with >0.97 accuracy. However, significantly more information is ripe for extraction from
malaria smears beyond the simple detection of parasites. We built an early prototype of a 2nd-generation CV
algorithm capable of identifying the correct parasite stage to the level of early, middle or late ring stage with
>0.80 accuracy, and in this proposal we aim to refine the performance and extend the capabilities of the
malaria CV system to wider applications while pioneering new computational methods in multiple domain
adaptation and weakly- and semi-supervised learning.
 The proposed project would result in the development of a next-generation malaria CV system that can
derive molecular data from brightfield images for use by investigators at the bench or in the clinic. We will build
out the prototype CV system to optimize performance, develop higher-order classifiers (e.g., differentiating
viable from nonviable circulating parasites, finding once-infected cells for the prognosis of delayed hemolysis
after treatment), and run the algorithm against different tissue backgrounds (e.g., liver, spleen). The product of
this work will be a cutting-edge neural network-based malaria CV system that provides a multiplex readout of
parasite biological parameters and cellular pathology to help propel the fields of malaria research and
biomedical CV analysis forward.

## Key facts

- **NIH application ID:** 10914776
- **Project number:** 5R21AI169363-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Benjamin D Haeffele
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $196,745
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10914776

## Citation

> US National Institutes of Health, RePORTER application 10914776, Computer Vision for Malaria Microscopy: Automated Detection and Classification of Plasmodium for Basic Science and Pre-Clinical Applications (5R21AI169363-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10914776. Licensed CC0.

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