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

> **NIH NIH R33** · PARASITE ID, CORP. · 2024 · $268,417

## Abstract

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.

## Key facts

- **NIH application ID:** 10884444
- **Project number:** 5R33TW011753-05
- **Recipient organization:** PARASITE ID, CORP.
- **Principal Investigator:** Kiersten Henderson
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $268,417
- **Award type:** 5
- **Project period:** 2020-09-14 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10884444

## Citation

> US National Institutes of Health, RePORTER application 10884444, A Machine Learning-Based Mobile Application and Cloud Platform to Enable Accurate and Streamlined Surveillance of Soil-Transmitted Helminth Infection and Schistosomiasis (5R33TW011753-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10884444. Licensed CC0.

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