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Record W7117249453 · doi:10.1002/alz70856_101439

Imaging Dementia in African Populations: Closing the Health Equity Gap

2025· article· en· W7117249453 on OpenAlexaff
Olujide Oyeniran, K. Donald, Akintunde T. Orunmuyi, Thomas K. Karikari, Tavia E. Evans, Jasmine D. Cakmak, Ethan C Draper, Valentine Ucheagwu, Justin W. Hicks, Pedro Rosa‐Neto, Yasser Iturria Medina, Rufus Akinyemi, Simon M. Ametamey, L Liu, Sheila Waa, Chi Udeh‐Momoh, Ozioma C. Okonkwo, Udunna Anazodo

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsLawson Health Research InstituteMcGill University Health CentreWestern UniversityArtificial Intelligence in Medicine (Canada)McGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsClosing (real estate)DementiaEquity (law)Health equityMedical imagingPublic health

Abstract

fetched live from OpenAlex

BACKGROUND: In the next two decades, Africa is projected to have one of the highest number of people with Alzheimer's disease and related dementias (ADRD) (Figure 1). Neuroimaging tools, particularly magnetic resonance imaging (MRI) and positron emission tomography (PET), are established diagnostic tools for characterizing ADRD. However, the use of these tools is unevenly distributed globally and acutely lacking in Africa, particularly in Sub-Saharan Africa. Because ADRD prevalence and risk factors vary across populations and geospatial lines, there is a growing need to develop neuroimaging capacity for dementia research in diverse populations to expand our understanding of its characteristics. Here, we highlight gaps in dementia imaging research globally, identify associated barriers to their use in Africa, and provide a perspective on opportunities to enable dementia neuroimaging research across Africa. METHOD: We reviewed published data from literature and clinical trial databases for ongoing or past ADRD studies around the world with PET or MRI. We then estimated the number of sites, participants, and capacity (participants/site). We also used published data to estimate Africa's imaging infrastructure and personnel needs and costs. RESULT: A total of 49 countries (∼25% of all countries in the world) account for the global ADRD neuroimaging research (Figure 2). On a regional level, Africa has the least number of ADRD study sites and participants who have/will have PET or MRI (Figure 2). In Africa, only 4 countries have conducted ADRD research using MRI and none have published research or reported ongoing clinical trials using PET. The challenges with acquiring and operating neuroimaging infrastructure including high costs of scanner and radiotracer production as well as shortage of skilled personnel are fundamental barriers to dementia imaging research in Africa. At minimum, $29,321,441,393 is required to equip Africa with the neuroimaging infrastructure and personnel to bridge the gap (Figure 3). CONCLUSION: Thus, we propose, 1) increased imaging infrastructure investment, especially in low-cost technologies, 2) optimization of existing clinical imaging systems for advanced imaging, 3) collaborative training of local personnel through upskilling programs and 4) establishment of regional and global partnerships. Together these actions can transform dementia imaging capacity in Africa.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.031
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.064
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0020.003
Scholarly communication0.0040.010
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.082
GPT teacher head0.411
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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