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

Award Number: R33TW011753
ORGANIZATION: FOGARTY INTERNATIONAL CENTER
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/14/2020
PERIOD OF PERFORMANCE END DATE: 06/30/2025

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = -$190 )
2026	2024	PARASITE ID, CORP	7336 16TH AVE NW	SEATTLE	WA	98117	KING	USA	International Research and Research Training	000	5	12/5/2025	NON-COMPETING CONTINUATION	-$190
														Subtotal = -$190

Issue Date FY: 2024 ( Subtotal = $268,417 )
2024	2024	PARASITE ID, CORP	7336 16TH AVE NW	SEATTLE	WA	98117	KING	USA	International Research and Research Training	000	5	7/8/2024	NON-COMPETING CONTINUATION	$268,417
														Subtotal = $268,417

Issue Date FY: 2023 ( Subtotal = $269,755 )
2023	2023	PARASITE ID, CORP.	7336 16TH AVE NW	SEATTLE	WA	98117	KING	USA	International Research and Research Training	001	4	7/7/2023	NON-COMPETING CONTINUATION	$269,755
2023	2022	PARASITE ID, CORP.	7336 16TH AVE NW	SEATTLE	WA	98117	KING	USA	International Research and Research Training	000	3	10/13/2022	EXTENSION WITH OR WITHOUT FUNDS	$0
														Subtotal = $269,755

Issue Date FY: 2022 ( Subtotal = $263,623 )
2022	2022	PARASITE ID, CORP.	7336 16TH AVE NW	SEATTLE	WA	98117	KING	USA	International Research and Research Training	000	3	8/15/2022	EXTENSION WITH OR WITHOUT FUNDS	$263,623
														Subtotal = $263,623

Grand Total All Awards = $801,605

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

Award Number: R33TW011753

ORGANIZATION: FOGARTY INTERNATIONAL CENTER

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/14/2020

PERIOD OF PERFORMANCE END DATE: 06/30/2025

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