# I-Corps: Optical design and the development of high accuracy automated tick classification using computer vision

> **NIH NIH R43** · VECTECH, LLC · 2022 · $55,000

## 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. Vectech is an NIH SBIR phase I awardee seeking to develop the
first automated imaging and identification system capable of instantaneously and accurately
identifying the top nine tick vectors in the US. The approach of standardized optical design and
development of a computer vision system offers several advantages over conventional
acarologist identification. This NIH I-Corps project seeks to improve understanding of tick
surveillance needs in the US. The proposed I-Corps team will focus on the commercial
opportunity to improve clinical decision making for administration of tick bite prophylaxis and
enhancing public health information for vector control organizations and the general public. The
resulting insights will be incorporated into Vectech’s future research with the aim of bringing a
commercial product to market.

## Key facts

- **NIH application ID:** 10561399
- **Project number:** 3R43AI162425-01A1S1
- **Recipient organization:** VECTECH, LLC
- **Principal Investigator:** Autumn Goodwin
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $55,000
- **Award type:** 3
- **Project period:** 2022-04-18 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10561399

## Citation

> US National Institutes of Health, RePORTER application 10561399, I-Corps: Optical design and the development of high accuracy automated tick classification using computer vision (3R43AI162425-01A1S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10561399. Licensed CC0.

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