Edge-tuning of artificial intelligence improves diagnostic performance for Schistosomiasis haematobium in a rural setting of Côte d’Ivoire
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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.
