Abstract
Tick species are considered the second leading vector of human diseases. Different ticks can transmit a variety of pathogens that cause various tick-borne diseases (TBD), such as Lyme disease. Currently, it remains a challenge to diagnose Lyme disease because of its non-specific symptoms. Rapid and accurate identification of tick species plays an important role in predicting potential disease risk for tick-bitten patients, and ensuring timely and effective treatment. Here, we developed, optimized, and tested a smartphone-based deep learning algorithm (termed “TickPhone app”) for tick identification. The deep learning model was trained by more than 2000 tick images and optimized by different parameters, including normal sizes of images, deep learning architectures, image styles, and training–testing dataset distributions. The optimized deep learning model achieved a training accuracy of ~90% and a validation accuracy of ~85%. The TickPhone app was used to identify 31 independent tick species and achieved an accuracy of 95.69%. Such a simple and easy-to-use TickPhone app showed great potential to estimate epidemiology and risk of tick-borne disease, help health care providers better predict potential disease risk for tick-bitten patients, and ultimately enable timely and effective medical treatment for patients.
Highlights
Most cases of Lyme disease are caused by Borrelia burgdorferi (B. burgdorferi) and, rarely, Borrelia mayonii, which can be transmitted by infected Ixodes scapularis ticks, commonly known as deer ticks [9]
Our study showed that the optimized deep learning model can achieve a training accuracy of
In the independent test with 31 tick specimens, our TickPhone app achieved an identification accuracy of 95.69%
Summary
Tick-borne diseases have become more prevalent in the United States in recent years, posing a growing threat to public health [1,2,3]. Various ticks can carry and transmit different pathogens that can cause more than 20 diseases in humans, including Lyme disease, granulocytic anaplasmosis, babesiosis, and Borrelia mayonii infection [4,5,6]. Lyme disease represents the most frequent tick-borne disease in the United States, with at least 30,000 cases reported each year according to the Centers for Disease Control and Prevention [7]. Most cases of Lyme disease are caused by Borrelia burgdorferi (B. burgdorferi) and, rarely, Borrelia mayonii, which can be transmitted by infected Ixodes scapularis ticks, commonly known as deer ticks [9]. Rapid and accurate tick identification plays an important role in: (i) estimating epidemiology and risk of tick-borne diseases [12,13], and (ii) helping health care providers better predict potential disease risk for tick-bitten patients, reducing unnecessary prescription of prophylactic antibiotics [9,14,15]
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