Automated identification of tick species using advanced technology
Official title: High accuracy automated tick classification using computer vision
Funded at Vectech, LLC · NIH-11062363 · Data updated 2026-11-12
This study is working on a smart computer system that can quickly and accurately identify different types of ticks, which will help keep track of them and prevent diseases like Lyme disease, making it easier for everyone to stay healthy in areas where ticks are common.
Quick facts
| Grant type | Sbir 2 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Vectech, LLC NIH-funded |
| Lab location | 1 site (Baltimore, UNITED STATES) |
| Project start | April 18, 2022 |
| Project end | April 30, 2026 |
| Funding year | 2025 |
| Award (this year) | $970,682 |
| NIH project number | 5R44AI162425-04 |
| Project ID | NIH-11062363 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals living in or frequently visiting areas with high tick populations, particularly those at risk for tick-borne diseases.
✕Less likely to help
Patients who do not live in tick-endemic areas or who are not at risk for tick-borne diseases may not benefit from this research.
What this research studies
This research focuses on developing an automated system to classify tick species using computer vision technology. By leveraging artificial intelligence, the project aims to enhance tick vector surveillance, which is crucial for preventing tick-borne diseases like Lyme disease. The methodology involves creating an advanced imaging system that can accurately identify ticks, reducing the reliance on expert taxonomists and improving the efficiency of tick monitoring efforts. This could lead to better public health responses in areas where tick-borne diseases are prevalent.
Why it matters
Potential benefit: If successful, this research could significantly improve the accuracy and efficiency of tick identification, leading to better prevention strategies for tick-borne diseases.
How similar studies have gone: While there has been some research on mobile applications for tick reporting, this automated approach to tick identification is novel and has not been widely tested.
Where this research is happening
-
Vectech, LLC
Baltimore, United States Active
Researchers
- Principal investigator: Goodwin, Autumn — Vectech, LLC
- Study coordinator: Goodwin, Autumn
Related research
Other NIH-funded projects on the same topics.
-
Using computer vision to identify mosquito larvae species
This study is working on a smart system that uses computer technology to quickly identify different types of mosquito larvae, helping to improve mosquito control efforts and keep you safe from bites.
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.