A dual-modal machine learning framework integrating red blood indices and smartphone-captured microscopic images for β-thalassemia screening

Summary

A dual-modal screening pipeline combining red blood cell indices with smartphone-captured microscopic images for β-thalassemia carrier detection. The RBC-indices model alone reached 88.2% sensitivity and 92.9% specificity; the image-analysis model reached 88.2% sensitivity and 64.3% specificity. Run sequentially, the combined pipeline reached 100% sensitivity and 60.7% specificity, cutting the need for blood smear preparation by 37.7% - a practical, low-cost route to mass carrier screening in high-prevalence settings like Sri Lanka.