Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). Kato-Katz technique is the diagnosis method recommended by WHO and although is generally more sensitive than other microscopic methods in high transmission settings, it often presents a decreased sensitivity in low transmission settings and it is labour intensive. Digitizing the samples could provide a solution which allows to store the samples in a digital database and perform remote analysis. Artificial intelligence methods based on digitized samples can support diagnostics efforts by support diagnostics efforts by performing an automatic and objective quantification of disease infection. In this work, we propose an end-to-end pipeline for microscopy image digitization and automatic analysis of digitized images of soil-transmitted helminths. Our solution includes (1) a digitalization system based on a mobile app that digitizes the microscope samples using a low-cost 3D-printed microscope adapter, (2) a telemedicine platform for remote analysis and labelling and (3) novel deep learning algorithms for automatic assessment and quantification of parasitological infection of STH. This work has been evaluated by comparing the STH quantification using both a manual remote analysis based on the digitized images and the AI-assisted quantification against the reference method based on conventional microscopy. The deep learning algorithm has been trained and tested on 41 slides of stool samples containing 949 eggs from 6 different subjects using a cross-validation strategy obtaining a mean precision of 98,44% and mean recall of 80,94%. The results also proved the potential of generalization capability of the method at identifying different types of helminth eggs. In conclusion, this work has presented a comprehensive pipeline using smartphone-based microscopy integrated with a telemedicine platform for automatic image analysis and quantification of STH infection using artificial intelligence models.

中文翻译：

---

深度学习辅助的移动显微镜和远程医疗平台，用于量化Trichuris trichiura感染。

土壤传播的蠕虫（STH）是被忽视的热带病（NTD）中最普遍的病原体。Kato-Katz技术是WHO推荐的诊断方法，尽管通常在高透射率设置下比其他显微镜方法更敏感，但在低透射率设置下通常会降低灵敏度，并且劳动强度大。将样本数字化可以提供一种解决方案，该解决方案允许将样本存储在数字数据库中并执行远程分析。基于数字化样本的人工智能方法可以通过执行疾病感染的自动和客观量化来支持诊断工作，从而支持诊断工作。在这项工作中 我们提出了一种用于显微镜图像数字化和对土壤传播的蠕虫的数字化图像进行自动分析的端到端管道。我们的解决方案包括（1）基于移动应用程序的数字化系统，该系统使用低成本的3D打印显微镜适配器将显微镜样品数字化；（2）用于远程分析和标记的远程医疗平台；以及（3）新型的深度学习算法，用于自动评估和量化STH的寄生虫感染。通过使用基于数字化图像的手动远程分析和基于人工智能的AI辅助定量与基于常规显微镜的参考方法比较STH定量，对这项工作进行了评估。深度学习算法已通过交叉验证策略在41个载有6个不同受试者的949个卵的粪便样本幻灯片上进行了训练和测试，获得的平均精度为98.44％，平均召回率为80.94％。结果还证明了该方法在识别不同类型的蠕虫卵方面的潜在能力。总之，这项工作提出了一个综合的流程，该流程使用基于智能手机的显微镜技术与远程医疗平台相集成，用于使用人工智能模型对STH感染进行自动图像分析和定量。