A schistosomiasis dataset with bright- and darkfield images
Bibliographic record
Abstract
Schistosomiasis is a neglected tropical disease (NTD) that threatens 700 million and impacts 250 million people per year.The disease is caused by blood flukes of the genus Schistosoma, which enter the human body through contact with infected water.One species, S. haematobium, sheds eggs through the urinary tract, and can thus be diagnosed by examining urine samples for these eggs.Because concentrations of schistosomiasis infection are highly localized and are often in remote areas, rapid and robust field diagnosis is crucial to both individual diagnosis and the mapping that informs control efforts.Artificial intelligence (AI) algorithms, if properly designed, can speed up and improve both diagnosis and mapping through scalable, accurate analysis of images of urine samples.To develop such algorithms, we offer the dataset described here.It consists of paired bright-and darkfield images of urine samples collected in two distinct field studies in Côte d'Ivoire, Africa.There are images from 728 patients, of whom 151 were schisto-positive and contain S. haematobium eggs.Crucially, each patient has sufficient images to diagnose S. haematobium infection, so the dataset can be used to realistically test the diagnostic value of algorithms for clinical use.The division into two studies allows testing of algorithm generalizability.Due to exigencies of the data collection protocol, the images display a variety of qualities, from clear to blurry, which further allows testing of algorithm robustness to realistic noise.The dataset is thus well-suited to developing algorithms that can be of concrete value in schistosomiasis control efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".