A Machine Learning-Based Mobile Application and Cloud Platform to Enable Accurate and Streamlined Surveillance of Soil-Transmitted Helminth Infection and Schistosomiasis

基于机器学习的移动应用程序和云平台，可准确、简化地监测土源性蠕虫感染和血吸虫病

• 批准号: 10260544
• 负责人: Kiersten Henderson
• 金额: $ 16.53万
• 依托单位: PARASITE ID, CORP.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-14 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10260544
• 关键词: Affect; Algorithms; Architecture; Car Phone; Classification; Collection; Cost Savings; Data; Data Aggregation; Data Collection; Data Scientist; Data Store; Databases; Decision Making; Feedback; Future; Geographic Locations; Goals; Health; Helminths; Image; Infection Control; Internet; Intervention; Location; Machine Learning; Microscope; Microscopic; Microscopy; Modeling; Pain; Parasites; Parasitic infection; Phase; Prevention; Prevention program; Process; Publishing; Reporting; Research Personnel; Schistosoma; Schistosomiasis; Soil; Speed; Standardization; Surface; System; Testing; Time; Validation; Work; algorithm training; base; cloud based; cloud platform; cloud storage; convolutional neural network; cost; dashboard; data access; data integration; data visualization; deep learning; design; diagnostic platform; digital; disorder control; egg; global health; helminth infection; improved; machine learning algorithm; microscopic imaging; mobile application; prevent; product development; stool sample; surveillance data; tool; transmission process; usability; validation studies; web app

PROJECT SUMMARY/ABSTRACT Soil-transmitted helminth (STH) infections and schistosomiasis affect 2 billion people and have significant detrimental effects on health. Strategies to implement STH and schistosomiasis interventions currently rely on testing for these parasites by microscopic analysis of stool samples to detect parasite eggs and identify egg species. Accurate surveillance testing and timely and accurate reporting of results are required for effective decision-making at the programmatic level to implement infection control strategies. Approaches that increase the speed and standardize the accuracy of microscopy-based testing and streamline reporting could help eliminate STH infections and schistosomiasis. We propose to develop a mobile phone-based STH-schistosome egg identification and counting tool that employs machine learning (deep learning) and works in the absence of an internet connection. With this app, users will collect surveillance data for integration into a cloud platform. Surveillance data can then be visualized in dashboards to inform interventions to control disease. Our approach is fundamentally different from other published work that develop machine learning algorithms for STH and schistosomiasis because it will very accurately identify egg types during surveillance activities, and it will be available to users in an app and integrate with cloud storage and reporting. Our interdisciplinary team combines the expertise of global health researchers, product usability testing experts, microscopists, and data scientists. In the R21 phase, we will collect the largest ever microscopy image set of STH and schistosome eggs (> 15 000). We will train an algorithm based on convolutional neural networks that make highly accurate parasite egg classification (species identification) and embed this algorithm into a mobile app that works without internet connectivity. To promote app utility, we will evaluate its accuracy and usability in a surveillance setting. We established the feasibility of our approach in preliminary data by building a web app that serves the results of a deep learning model that identifies STH and schistosome eggs with > 98% accuracy. The R33 phase will be only undertaken if well-defined milestones are achieved. We will further develop the mobile app as a data capture system that will integrate with cloud storage and a dynamic data visualization system to enable increased accuracy in STH and schistosomiasis surveillance over time and across geographic location. ​Validation studies will assess the​ benefits of the system to time and cost savings and quality of data collected during surveillance activities. The overall goal of this work is to increase the accuracy and streamline STH and schistosomiasis surveillance to enable effective decision-making in disease control.

项目总结/摘要 土壤传播蠕虫（STH）感染和血吸虫病影响20亿人， 对健康的有害影响。目前实施性病和血吸虫病干预措施的战略依赖于 通过粪便样本的显微镜分析检测这些寄生虫，以检测寄生虫卵并识别卵 物种需要准确的监测检测和及时准确的结果报告， 方案一级的决策，以执行感染控制战略。增加的方法 基于显微镜的测试的速度和标准化的准确性以及简化的报告可能会有所帮助 消灭性病和血吸虫病。 我们建议开发一种基于移动的手机的STH-虫卵识别和计数工具， 采用机器学习（深度学习），在没有互联网连接的情况下工作。有了这个应用程序， 用户将收集监控数据，并将其整合到云平台。监控数据可以可视化 在仪表板中，为控制疾病的干预措施提供信息。 我们的方法与其他开发机器学习算法的已发表工作有根本不同 因为它可以在监测活动中非常准确地识别虫卵类型， 它将在应用程序中提供给用户，并与云存储和报告集成。我们的跨学科 该团队结合了全球健康研究人员、产品可用性测试专家、显微镜专家和 数据科学家 在R21阶段，我们将收集有史以来最大的STH和虫卵显微镜图像集（> 15 000）。我们将训练一种基于卷积神经网络的算法，使高度准确的寄生虫卵 分类（物种识别），并将该算法嵌入到无需互联网即可工作的移动的应用程序中 连通性。为了提高应用程序的实用性，我们将评估其在监控环境中的准确性和可用性。我们 建立了我们的方法的可行性，在初步的数据，通过建立一个网络应用程序，服务的结果， 深度学习模型，以> 98%的准确率识别STH和寄生虫卵。 R33阶段将仅在实现明确定义的里程碑时进行。我们会进一步发展 移动的应用程序作为数据捕获系统，将与云存储和动态数据可视化集成 一个能够提高STH和血吸虫病监测准确性的系统， 地理位置 验证研究将评估该系统在节省时间和费用方面的好处， 监测活动期间收集的数据的质量。这项工作的总体目标是提高准确性 简化性病和血吸虫病监测，以便在疾病控制方面作出有效决策。