A Computer Vision Approach to Identifying Ticks Related to Lyme Disease

IEEE Journal of Translational Engineering in Health and Medicine

Vanessa G. Allen¹  Samir N. Patel¹  Curtis B. Russell²  Mark P. Nelder²  Laurent Moreno³  Tania Cawston⁴  Sina Akbarian⁵  Elham Dolatabadi⁶ 

[1] Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada;Enteric, Zoonotic and Vector-Borne Diseases, Health Protection, Operations and Response, Public Health Ontario, Toronto, Canada;Innovations and Partnerships Office, University of Toronto, Toronto, Canada;Public Health Laboratories, Public Health Ontario, Sault Ste. Marie, Canada;Public Health Ontario, Toronto, Canada;Vector Institute for Artificial Intelligence, Toronto, Canada;

关键词: Computer vision;    convolution neural network;    infectious disease;    Ixodes scapularis;    knowledge transfer;    Lyme disease;   

DOI  :  10.1109/JTEHM.2021.3137956

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown