Edge-tuning of artificial intelligence improves diagnostic performance for <i>Schistosomiasis haematobium</i> in a rural setting of Côte d’Ivoire
Bibliographic record
Abstract
Abstract Background Schistosomiasis affects over 200 million people and causes significant urogenital and gastrointestinal morbidity. Mass drug administration (MDA) with praziquantel is used to mitigate severe illness and reduce infection rates. Portable microscopy, combined with artificial intelligence (AI), offers a novel method for schistosomiasis screening in low-resource settings. This study tested whether re-training AI models for Schistosoma egg detection with local field data, a process we call “edge-tuning”, could improve the model’s performance on the following field day. Methods This study in Côte d’Ivoire evaluated a portable microscope (NTDscope) for Schistosoma haematobium screening. Urine samples from 100 community members were analyzed using AI models on the NTDscope and traditional light microscopy. Starting AI models, trained on images from a previous version of the NTDscope, were edge-tuned after the first day of sample collection using cloud-based image annotation and re-training. Starting and edge-tuned models were evaluated at confidence thresholds optimizing for sensitivity, specificity, or egg counting. Findings For all thresholds, edge-tuned models performed better than starting AI models. Compared to manual counting of eggs on the NTDscope, sensitivity of the starting AI model on day 2 ranged from 59.3%-75.5%, with specificity ranging from 46.7%-85.7%. After edge-tuning, sensitivity increased to 77.8%-100%, with specificity from 78.6%-100%. Compared to light microscopy, edge-tuned AI models had comparable performance to manual counting from NTDscope images. Interpretation Portable microscopy is an effective solution for rapid, on-site schistosomiasis screening. AI- based egg detection increases diagnostic throughput while maintaining good performance. This study demonstrates that edge-tuning AI models with local data significantly improves their performance and can be performed in low-resource settings, making the combined technologies effective tools for monitoring schistosomiasis programs in endemic areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".