A Framework for Advancing Sustainable MRI Access in Africa
Bibliographic record
Abstract
Summary MRI technology has profoundly transformed current healthcare and research systems globally. The rapidly growing burden of non-communicable diseases in Africa has underscored the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education & Research in Africa (CAMERA) is a network of African experts, global partners, and ISMRM/ESMRMB members implementing novel strategies to advance MRI access and research in Africa. To identify challenges to MRI usage and provide a framework for addressing MRI needs in the region, CAMERA conducted a Needs Assessment Survey (NAS) and a series of symposia at international MRI society meetings over a 2-year period. The 68-question NAS was distributed to MRI users in Africa and completed by 157 clinicians and scientists from across Sub-Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at <1, of which, 39% were obsolete low-field systems yet still in use to meet clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher-field (1.5T) MRI scanners in the region. However, these systems are underutilized with only 8% of facilities reporting clinical scans of 15 or more patients daily per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. Our NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up-to-date data on MRI density in Africa and offers unique insight into Africa’s MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high-value MRI access in SSA. Findings from the NAS and focused discussions at ISMRM and ESMRMB provided the basis for the framework presented here for advancing MRI capacity in SSA. Graphical Abstract Africa has a massive population with few infrastructural resources and an untapped potential to effect transformative change in healthcare. To advance MRI access across all African countries and meet the sustainable development goals of improving health and wellbeing in low-resource settings over the next decade, the MRI community is called to partner with CAMERA to create enabling clinical and research MRI environments that will utilize the rich intellectual resources in Africa to realize lasting and equitable MRI for all Africans and the world at large.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".