Malaria RDT (mRDT) interpretation accuracy by frontline health workers compared to AI in Kano state, Nigeria.

ABSTRACT:

Background

Although malaria is preventable and treatable, it continues to be a significant cause of illness and death. Early diagnosis through testing is critical in reducing malaria-related morbidity and mortality. Malaria rapid diagnostic tests (mRDTs) are preferred for their ease of use, sensitivity, and rapid results, yet misadministration and misinterpretation errors persist. This study investigated whether pairing an existing application with an AI-based software could enhance interpretation accuracy among Frontline Healthcare Workers (FHWs) in Kano State, Nigeria.

Methods

A comparative analysis was conducted, examining mRDT interpretations by FHWs, trained expert mRDT reviewers (Panel Readers), and AI-based computer vision algorithms. The accuracy comparisons included

SUBMITTER: Frade S 

PROVIDER: S-EPMC12460424 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature