Sina Akbarian1,2, Mark P Nelder3, Curtis B Russell3, Tania Cawston4, Laurent Moreno5, Samir N Patel6,7, Vanessa G Allen6,7, Elham Dolatabadi2,8. 1. Public Health Ontario Toronto ON M5G 1M1 Canada. 2. Vector Institute for Artificial Intelligence Toronto ON M5G 1M1 Canada. 3. Enteric, Zoonotic and Vector-Borne Diseases, Health Protection, Operations and ResponsePublic Health Ontario Toronto ON M5G 1M1 Canada. 4. Public Health LaboratoriesPublic Health Ontario Sault Ste. Marie ON P6B 0A9 Canada. 5. Innovations and Partnerships OfficeUniversity of Toronto Toronto ON M5S 1A1 Canada. 6. Department of Laboratory Medicine and PathobiologyUniversity of Toronto Toronto ON M5S 1A1 Canada. 7. Medical MicrobiologyPublic Health Ontario Toronto ON M5G 1M1 Canada. 8. Institute of Health Policy, Management and Evaluation, University of Toronto Toronto ON M5S 1A1 Canada.
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.
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.
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