Barriers and opportunities to improve renal outcomes in South Africa using AI technology for pediatric ultrasound interpretation
Bibliographic record
Abstract
Over 10% of the global population is affected by chronic kidney disease (CKD) and those without preventative care and early intervention are the worst impacted. Many childhood precursors to CKD such as hydronephrosis (HN) continue to be detected and treated late in low- and middle-income countries where prenatal and early-life ultrasound is less common. Artificial intelligence-based technology holds promise for improving some of this detection and treatment. In this work, we explore the barriers and opportunities of transferring an AI-based tool for early HN detection in pediatric ultrasound from Canada, where it was initially developed, to South Africa. We explore these challenges and opportunities at the health-system-, institutional-, and provider-levels. Our investigation is performed through interviews with clinicians at various levels, locations, and in different specialties. We find that the context of our tool’s use will change in terms of both clinicians and patients, as the users of our tool in South Africa will have less access to pediatric sonography expertise and, for related reasons, patients will tend to be older when they receive an ultrasound imaging. These differences indicate that while the initial algorithm can be tested and fine-tuned in certain settings, there is a larger need for tools which make standardized ultrasound easier to acquire. The clinicians interviewed are eager for AI-based assistance in caring patients earlier and more effectively and believe algorithms of this kind will be useful for improving care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".