A Computer Vision Approach to Identifying Ticks Related to Lyme Disease

IEEE J Transl Eng Health Med. 2021 Dec 30:10:4900308. doi: 10.1109/JTEHM.2021.3137956. eCollection 2022.

Abstract

Background: Lyme disease (caused by Borrelia burgdorferi) is an infectious disease transmitted to humans by a bite from infected blacklegged ticks (Ixodes scapularis) in eastern North America. Lyme disease can be prevented if antibiotic prophylaxis is given to a patient within 72 hours of a blacklegged tick bite. Therefore, recognizing a blacklegged tick could facilitate the management of Lyme disease. Methods: In this work, we build an automated detection tool that can differentiate blacklegged ticks from other tick species using advanced computer vision approaches in real-time. Specially, we use convolution neural network models, trained end-to-end, to classify tick species. Also, advanced knowledge transfer techniques are adopted to improve the performance of convolution neural network models. Results: Our best convolution neural network model achieves 92% accuracy on unseen tick species. Conclusion: Our proposed vision-based approach simplifies tick identification and contributes to the emerging work on public health surveillance of ticks and tick-borne diseases. In addition, it can be integrated with the geography of exposure and potentially be leveraged to inform the risk of Lyme disease infection. This is the first report of using deep learning technologies to classify ticks, providing the basis for automation of tick surveillance, and advancing tick-borne disease ecology and risk management.

Keywords: Computer vision; Ixodes scapularis; Lyme disease; convolution neural network; infectious disease; knowledge transfer; public health; surveillance; vector-borne disease.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Borrelia burgdorferi*
  • Computers
  • Humans
  • Ixodes*
  • Lyme Disease* / diagnosis
  • Tick-Borne Diseases* / diagnosis

Grants and funding

This work was supported by the Vector Institute and Public Health Ontario through Pathfinder Projects.