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.