Department of Physics, College of Liberal Arts and Sciences, University of Wisconsin-Madison, Madison, WI, United States of America.
Department of Entomology, College of Agricultural and Life Sciences, University of Wisconsin-Madison, Madison, WI, United States of America.
PLoS One. 2021 Dec 2;16(12):e0260622. doi: 10.1371/journal.pone.0260622. eCollection 2021.
Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real-time tick-image identification through a smartphone app or similar platform could help mitigate this threat by informing users of the risks associated with encountered ticks and by providing researchers and public health agencies with additional data on tick activity and geographic range. Here we outline the requirements for such a system, present a model that meets those requirements, and discuss remaining challenges and frontiers in automated tick identification. We compiled a user-generated dataset of more than 12,000 images of the three most common tick species found on humans in the U.S.: Amblyomma americanum, Dermacentor variabilis, and Ixodes scapularis. We used image augmentation to further increase the size of our dataset to more than 90,000 images. Here we report the development and validation of a convolutional neural network which we call "TickIDNet," that scores an 87.8% identification accuracy across all three species, outperforming the accuracy of identifications done by a member of the general public or healthcare professionals. However, the model fails to match the performance of experts with formal entomological training. We find that image quality, particularly the size of the tick in the image (measured in pixels), plays a significant role in the network's ability to correctly identify an image: images where the tick is small are less likely to be correctly identified because of the small object detection problem in deep learning. TickIDNet's performance can be increased by using confidence thresholds to introduce an "unsure" class and building image submission pipelines that encourage better quality photos. Our findings suggest that deep learning represents a promising frontier for tick identification that should be further explored and deployed as part of the toolkit for addressing the public health consequences of tick-borne diseases.
蜱虫和蜱传疾病对北美和欧洲的公共卫生构成日益严重的威胁。蜱虫的数量、它们的地理分布以及蜱传疾病（如莱姆病）的发病率都在上升。通过智能手机应用程序或类似平台实现准确、实时的蜱虫图像识别，可以帮助减轻这一威胁，让用户了解遇到的蜱虫带来的风险，并为研究人员和公共卫生机构提供有关蜱虫活动和地理分布范围的额外数据。本文概述了此类系统的要求，提出了一个满足这些要求的模型，并讨论了自动蜱虫识别中存在的挑战和前沿问题。我们收集了超过 12000 张在美国最常见的三种人用蜱虫（美洲钝眼蜱、草原革蜱和肩突硬蜱）的图像，创建了一个用户生成的数据集。我们使用图像增强进一步将数据集的大小增加到超过 90000 张图像。在此，我们报告了卷积神经网络（我们称之为“TickIDNet”）的开发和验证，该网络在所有三种蜱虫上的识别准确率达到 87.8%，优于普通公众或医疗保健专业人员的识别准确率。然而，该模型未能达到具有正式昆虫学培训的专家的性能。我们发现图像质量，尤其是蜱虫在图像中的大小（以像素为单位），对网络正确识别图像的能力有重要影响：蜱虫较小的图像不太可能被正确识别，这是因为深度学习中的小物体检测问题。通过使用置信度阈值引入“不确定”类别并构建鼓励提供更高质量照片的图像提交管道，可以提高 TickIDNet 的性能。我们的研究结果表明，深度学习代表了蜱虫识别的一个有前途的前沿领域，应该进一步探索和部署，作为应对蜱传疾病公共卫生后果的工具包的一部分。
