Hopkins Marine Station, Stanford University, Pacific Grove, CA, United States.
Centre de Recherche Biomédicale Espoir pour la Santé, Saint-Louis, Senegal.
Front Public Health. 2021 Jul 15;9:642895. doi: 10.3389/fpubh.2021.642895. eCollection 2021.
In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans snail intermediate hosts. The parasite affects more than 200 million people in tropical and subtropical regions. We trained our CNN, a feed-forward neural network, on a limited dataset of 5,500 images of snails and 5,100 images of cercariae obtained from schistosomiasis transmission sites in the Senegal River Basin, a region in western Africa that is hyper-endemic for the disease. The image set included both images of two snail genera that are relevant to schistosomiasis transmission - that is, spp. and - as well as snail images that are non-component hosts for human schistosomiasis. Cercariae shed from spp. snails were classified into 11 categories, of which only two, and , are major etiological agents of human schistosomiasis. The algorithms, trained on 80% of the snail and parasite dataset, achieved 99% and 91% accuracy for snail and parasite classification, respectively, when used on the hold-out validation dataset - a performance comparable to that of experienced parasitologists. The promising results of this proof-of-concept study suggests that this CNN model, and potentially similar replicable models, have the potential to support the classification of snails and parasite of medical importance. In remote field settings where machine learning algorithms can be deployed on cost-effective and widely used mobile devices, such as smartphones, these models can be a valuable complement to laboratory identification by trained technicians. Future efforts must be dedicated to increasing dataset sizes for model training and validation, as well as testing these algorithms in diverse transmission settings and geographies.
在最近几十年，计算机视觉在解决公共卫生领域的各种问题方面表现出了惊人的效果，从确定人类疾病的诊断、预后和治疗，到预测传染病的爆发。在这里，我们研究卷积神经网络（CNN）是否也能有效地对具有公共卫生重要性的寄生虫的环境阶段及其无脊椎宿主进行分类。我们使用血吸虫病作为参考模型。血吸虫病是一种使人衰弱的寄生虫病，通过中间宿主蜗牛传播给人类。该寄生虫影响了热带和亚热带地区的 2 亿多人。我们在一个有限的数据集上训练了我们的 CNN，该数据集由来自塞内加尔河流域血吸虫病传播地点的 5500 张蜗牛图像和 5100 张尾蚴图像组成，塞内加尔河流域是该疾病的高度流行地区。该图像集包括与血吸虫病传播相关的两种蜗牛属的图像，即 spp. 和 ，以及非人类血吸虫病的宿主蜗牛图像。从 spp. 蜗牛身上脱落的尾蚴被分为 11 类，其中只有 和 是人类血吸虫病的主要病原体。该算法在 80%的蜗牛和寄生虫数据集上进行了训练，当在保留的验证数据集上使用时，对蜗牛和寄生虫的分类准确率分别达到了 99%和 91%，这一性能与经验丰富的寄生虫学家相当。这项概念验证研究的有希望的结果表明，这种 CNN 模型，以及潜在的可复制模型，有可能支持对具有医学重要性的蜗牛和寄生虫的分类。在可以在成本效益高且广泛使用的移动设备（如智能手机）上部署机器学习算法的偏远实地环境中，这些模型可以成为经过培训的技术人员进行实验室鉴定的有价值的补充。未来的工作必须致力于增加模型训练和验证的数据集大小，并在不同的传播环境和地理环境中测试这些算法。
