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Article

TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning

1
Department of Biomedical Engineering, University of Connecticut Health Center, Farmington, CT 06030, USA
2
Connecticut Veterinary Medical Diagnostic Laboratory, Department of Pathobiology and Veterinary Science, University of Connecticut, Storrs, CT 06269, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Juan Ye and Francesco Bianconi
Appl. Sci. 2021, 11(16), 7355; https://doi.org/10.3390/app11167355
Received: 5 June 2021 / Revised: 31 July 2021 / Accepted: 2 August 2021 / Published: 10 August 2021
(This article belongs to the Special Issue Sensor-Based Human Activity Recognition in Real-World Scenarios)
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. View Full-Text
Keywords: tick identification; smartphone application; deep learning; Lyme disease tick identification; smartphone application; deep learning; Lyme disease
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MDPI and ACS Style

Xu, Z.; Ding, X.; Yin, K.; Li, Z.; Smyth, J.A.; Sims, M.B.; McGinnis, H.A.; Liu, C. TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning. Appl. Sci. 2021, 11, 7355. https://doi.org/10.3390/app11167355

AMA Style

Xu Z, Ding X, Yin K, Li Z, Smyth JA, Sims MB, McGinnis HA, Liu C. TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning. Applied Sciences. 2021; 11(16):7355. https://doi.org/10.3390/app11167355

Chicago/Turabian Style

Xu, Zhiheng, Xiong Ding, Kun Yin, Ziyue Li, Joan A. Smyth, Maureen B. Sims, Holly A. McGinnis, and Changchun Liu. 2021. "TickPhone App: A Smartphone Application for Rapid Tick Identification Using Deep Learning" Applied Sciences 11, no. 16: 7355. https://doi.org/10.3390/app11167355

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