# Optical design and the development of high accuracy automated tick classification using computer vision

> **NIH NIH R43** · VECTECH, LLC · 2021 · $295,705

## Abstract

Abstract. The incidence of US tick-borne diseases has more than doubled in the last two
decades. Due to lack of effective vaccines for tick-borne diseases, prevention of tick bites
remains the primary focus of disease mitigation. Tick vector surveillance—monitoring an area to
understand tick species composition, abundance, and spatial distribution—is key to providing
the public with accurate and up-to-date information when they are in areas of high risk, and
enabling precision vector control when necessary. Despite the importance of vector
surveillance, current practices are highly resource intensive and require significant labor and
time to collect and identify vector specimens. Acarologist or field taxonomist expertise is a
limited resource required for tick identification, creating a significant capability barrier for
national tick surveillance practice. While mobile applications to facilitate passive surveillance
and reporting of human-tick encounters have grown in popularity, variable image quality, limited
engagement, and scientist misidentification of rare, invasive, or morphologically similar tick
species hinder the scalability of this approach. No automated solutions exist to build tick
identification capacity. We seek to develop the first imaging and automated identification system
capable of instantaneously and accurately identifying the top nine tick vectors in the US. This
proposal will first characterize the optical requirements necessary to image diagnostic
morphological features associated with adult ticks and develop a standardized imaging platform
for tick identification. This will enable the development of a high-quality tick image dataset in
partnership with the Walter Reed Biosystems Unit (WRBU) which will be used to train
high-accuracy computer vision models for tick species and sex identification. Ultimately the
approaches developed here will enable new tick identification tools for both the lab and citizen
scientists; allowing vector surveillance managers to leverage image recognition in a practical
system that will increase capacity and capability for biosurveillance, and equipping citizen
scientists with improved tools to identify tick species during a human-tick encounter.

## Key facts

- **NIH application ID:** 10325667
- **Project number:** 1R43AI162425-01A1
- **Recipient organization:** VECTECH, LLC
- **Principal Investigator:** Autumn Goodwin
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $295,705
- **Award type:** 1
- **Project period:** 2021-06-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10325667

## Citation

> US National Institutes of Health, RePORTER application 10325667, Optical design and the development of high accuracy automated tick classification using computer vision (1R43AI162425-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10325667. Licensed CC0.

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