ReviewBJR open2025
Case-based review of low-field MRI in resource-constrained settings: a clinical perspective from Malawi.
Review in BJR open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Low-field MRI (LF-MRI) is in the spotlight as multidisciplinary experts consider it to be one solution to expanding MRI access worldwide. The clinical scenarios and case-mix in which LF-MRI could play an especially important role in the patient diagnostic algorithm are different in High and Low- and Middle-Income Countries (LMIC). The aim of this article is to suggest a robust structure within which to envision clinical use and advancement of LF-MRI technology in LMICs. This article presents three discrete clinical scenarios-a tertiary care facility with an LF-MRI only, a tertiary care facility with an LF-MRI and an HF-MRI and a peripheral healthcare facility with an LF-MRI only-derived from a combination of the authors' observed practice and hypothetical models in an LMIC and 31 consecutive case reviews within a 32-month timeframe of our experience with the 0.064 T Hyperfine Swoop in Malawi. The authors recognize the important of a holistic approach to the ongoing multifaceted efforts at LMIC-appropriate advancement of LF-MRI technology. This ranges from continued innovation relating to deep learning methods for improved diagnostic accuracy and workflow efficiency, empowerment towards building LF-MRIs in-situ in the LMIC and multidisciplinary capacity building initiatives in LMICs.
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Registered trials
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