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Authors
Frade, Sasha https://orcid.org/0000-0001-9683-3020
Cooper, Shawna
Smedinghoff, Sam
Hattery, David
Ruan, Yongshao
Isabelli, Paul
Ravi, Nirmal
McLaughlin, Megan
Metz, Lynn
Finette, Barry
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Funding
Funding for this research was provided by:
Bill and Melinda Gates Foundation (INV-007492)
Audere
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Article History
Received: 4 January 2024
Accepted: 10 July 2025
First Online: 25 September 2025
Declarations
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: Ethics Approval was obtained from The University of Vermont Committees on Human Research (Ethics Number MOD00005335) and the Kano State Ministry of Health Research Ethics Committee (Ethics Number MOH/Off/797/T.I/2056).
: Not applicable.
: The authors declare that they have no competing interests. THINKMD was the principal investigator of the main study, authoring the study protocol and obtaining IRB. As part of an extension of the study that was submitted and approved by ethics, in partnership with Audere, THINKMD included an augmented version of the technology—which included Audere’s smartphone image capture and machine learning (ML) based rapid diagnostic test (RDT) analysis algorithms for malaria. THINKMD received funding from Audere to be able to incorporate this technology into their existing platform. This funding was part of grant received by Audere from the Gates Foundation.
