Using computer vision to improve malaria detection and classification.
Official title: Computer Vision for Malaria Microscopy: Automated Detection and Classification of Plasmodium for Basic Science and Pre-Clinical Applications
Funded at Johns Hopkins University · NIH-10914776 · Data updated 2026-11-12
This study is working on a smart computer program that can quickly and accurately find malaria parasites in blood samples, making it easier for doctors to diagnose and treat malaria, especially in areas where the disease is common.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Johns Hopkins University NIH-funded |
| Lab location | 1 site (Baltimore, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2024 |
| Award (this year) | $196,745 |
| NIH project number | 5R21AI169363-02 |
| Project ID | NIH-10914776 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are children under 11 years old who are suspected of having malaria.
✕Less likely to help
Patients who are not affected by malaria or are older than 11 years may not benefit from this research.
What this research studies
This research focuses on developing an advanced computer vision system to automate the detection and classification of malaria-causing parasites in blood samples. By utilizing deep learning algorithms, the project aims to enhance the speed and accuracy of malaria diagnosis, which is crucial for effective treatment. The approach involves training the system on a large dataset of microscopy images, allowing it to learn and identify subtle differences in parasite forms that are often missed by human experts. This technology could significantly reduce the time and labor involved in diagnosing malaria, making it more accessible, especially in regions heavily affected by the disease.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate malaria diagnoses, improving treatment outcomes for patients.
How similar studies have gone: Other research has shown success in using AI and computer vision for diagnosing infectious diseases, indicating a promising potential for this approach in malaria detection.
Where this research is happening
-
Johns Hopkins University
Baltimore, United States Active
Researchers
- Principal investigator: Ippolito, Matthew Michael — Johns Hopkins University
- Study coordinator: Ippolito, Matthew Michael
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.