A Computer Vision Approach to Identifying Ticks Related to Lyme Disease.

Sina Akbarian,Laurent Moreno,Tania Cawston,Curtis B Russell,Mark P Nelder,Samir N Patel,Vanessa G Allen,Elham Dolatabadi

doi:10.1109/jtehm.2021.3137956

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.

Highlights

Lyme disease is caused by the spirochete Borrelia burgdorferi sensu stricto and is spread to humans through the bite of an infected blacklegged tick (Ixodes scapularis) in most of eastern North America
Lyme disease is the most common tick-borne disease in North America, and in the United States (US), approximately 475,000 cases occur per year (20102018), with about 35,000 of these reported through reportable disease surveillance [1], [2]
The classification results obtained by applying different Convolution neural network (CNN) models on the tick data set are presented

Summary

Introduction

Lyme disease is caused by the spirochete Borrelia burgdorferi sensu stricto and is spread to humans through the bite of an infected blacklegged tick (Ixodes scapularis) in most of eastern North America. Lyme disease is the most common tick-borne disease in North America, and in the United States (US), approximately 475,000 cases occur per year (20102018), with about 35,000 of these reported through reportable disease surveillance [1], [2]. The increase in Lyme disease incidence is associated with the expanding range of the blacklegged tick, driven in part by climate change; i.e., an increase in annual cumulative degree days above 0◦C [4]. Lyme disease generally begins with influenza-like symptoms such as arthralgia, chills, fever, myalgia, and stiff neck, with the appearance of an erythema migrans rash 2 to 30.

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Results

Discussion

Conclusion