A Machine Learning-Based Mobile Application and Cloud Platform to Enable Accurate and Streamlined Surveillance of Soil-Transmitted Helminth Infection and Schistosomiasis
The following grant was awarded by, is supported by, is administered by or is in partnership with the Fogarty International Center at the U.S. National Institutes of Health (NIH).
Funding Fogarty Program
Mobile Health (mHealth)
Project Information in NIH RePORTER
A Machine Learning-Based Mobile Application and Cloud Platform to Enable Accurate and Streamlined Surveillance of Soil-Transmitted Helminth Infection and Schistosomiasis
Principal Institution
Parasite ID, Corp.
Principal Investigator(s) (PI)
Henderson, Kiersten
Project Contact Information
Email:
kiersten.henderson@gmail.com
Year(s) Awarded
2020-2025
Project Description
Accurate surveillance testing in the field and timely and accurate reporting of results are required for effective decision-making by soil-transmitted helminth (STH) infection and schistosomiasis control and prevention programs. This project will develop and test a mobile phone-based STH-schistosomiasis diagnostic system that employs machine learning to very accurately identify and count parasite eggs from microscopy images of stool samples. This mobile app will work in the absence of any internet connection and will streamline collection of surveillance data for integration into a cloud-based surveillance platform that increases data visibility.
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