Evaluating the performance of an artificial intelligence-based electronic reader for malaria rapid diagnostic tests across Benin, Cote d'Ivoire, Nigeria and Uganda.

ABSTRACT:

Background

The introduction of malaria rapid diagnostic tests (RDTs) has expanded the parasitological confirmation of malaria at all levels of health systems in sub-Saharan Africa, improving case management and surveillance. However, concerns persist regarding healthcare worker adherence to RDT outcomes and the accuracy of RDT results recorded in health facility registers. Electronic RDT readers have been proposed to improve the consistency of interpretation and reporting. The HealthPulse smartphone application (Audere, Seattle, WA, USA), an RDT reader using an artificial intelligence (AI) computer vision algorithm, was assessed against a trained human panel interpreting RDT results from photographs to determine the application's performance characteristics.

Methods

In 2023,

SUBMITTER: Lindblade KA 

PROVIDER: S-EPMC12486784 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature