ArticleBMC digital health2025
Malaria RDT (mRDT) interpretation accuracy by frontline health workers compared to AI in Kano state, Nigeria.
Article in BMC digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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: (1) AI interpretation versus Panel Read interpretation, (2) FHW interpretation versus Panel Read interpretation, (3) FHW interpretation versus AI interpretation, and (4) AI performance on faint positive lines. Accuracy was reported as a weighted F1 score, reflecting the harmonic mean of recall (sensitivity) and precision (positive predictive value). Results: The AI algorithm demonstrated high accuracy, matching Panel Read interpretations correctly for positives 96.38% of the time and negatives 97.12%. FHW interpretations agreed with the Panel Read 96.82% on positives and 94.31% on negatives. Comparison of FHW and AI interpretations showed 97.52% agreement on positives and 93.38% on negatives. The overall accuracy was higher for AI (weighted F1 score of 96.4) compared to FHWs (95.3). Notably, the AI accurately identified 90.2% of 163 faint positive mRDTs, whereas FHWs correctly identified 76.1%. Conclusion: AI-based computer vision algorithms performed comparably to trained and experienced FHWs and exceeded FHW performance in identifying faint positives. These findings demonstrate the potential of AI technology to enhance the accuracy of mRDT interpretation, thereby improving malaria diagnosis and reporting accuracy in malaria-endemic, resource-limited settings. Supplementary Information: The online version contains supplementary material available at 10.1186/s44247-025-00190-4.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.