Malaria RDT (mRDT) interpretation accuracy by frontline health workers compared to AI in Kano State, Nigeria [version 1]

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Introduction: Although malaria is preventable and treatable, it continues to be a significant cause of illness and death. One strategy found to mitigate this is early diagnosis through testing. Due to their ease of use, sensitivity, and rapid results, malaria RDTs (mRDTs) have become the preferred diagnostic test. However, misadministration and misinterpretation errors remain a concern. This study investigated whether RDT use could be paired with a mobile application to improve the accuracy of mRDT interpretations amongst Frontline Health Care Workers (FHWs) in Kano State, Nigeria.

The study performed a comparative analysis of mRDT interpretations by FHWs, trained mRDT reviewers (Panel Read), and AI based computer vision technology. We specifically compared the accuracy of 1) AI algorithms’ interpretation vs. Panel Read interpretation; 2) FHW interpretation vs. Panel Read interpretation; 3) FHW interpretation vs. AI algorithms’ interpretation; and 4) AI performance for faint positive

Conclusions: The AI performed as well as experienced and trained FHWs and performed even better than FHWs on faint lines. Therefore, AI computer vision technology can assist FHWs in accurately interpreting mRDTs and reporting results in highly malaria-endemic, low-resource settings, ensuring precise reporting of mRDT results.

Table 6: Overall accuracy of the AI algorithms for SD Bioline P.f.