Sickle cell disease (SCD) is the most prevalent inherited hematologic disorder worldwide. The global burden of SCD has markedly increased over the past two decades, with an estimated rise from 5.46 million (4.62–6.45) individuals in 2000 to 7.74 million (6.51–9.2) in 2021, and over 300,000 affected neonates born annually. The majority of cases occur in sub-Saharan Africa, where healthcare systems are under-resourced, and structural inequities limit access to diagnostic services and appropriate care, particularly among populations living below the poverty threshold and lacking social protection. Early identification of disease —through newborn screening and preconception testing—is critical. However, in high-burden, resource-limited settings, the implementation of such programs remains constrained by the cost and complexity of existing diagnostics, which often exceed 2 dollars per test. To address this diagnostic gap, we developed a novel, antibody-independent, ultralow-cost (<1 dollar), rapid (~20-minute), simple to handle yet highly efficient point-of-care assay. The test involves mixing a minimal volume of whole blood with a hyperosmolar buffer, subsequently applied to a glass coverslip. Erythrocyte dehydration, induced by the hyperosmolar buffer, increases intracellular hemoglobin concentration and promotes conformation-dependent interactions. These interactions, modulated by pathogenic hemoglobin variants, yield distinct desiccation-induced morphological signatures. These are captured via conventional smartphone photography and analyzed using a Random Forest-based machine learning classifier, enabling robust discrimination among hemoglobin phenotypes: normal (AA), carrier (AS), and disease (SS, SC). In a prospective cohort of 166 individuals, the assay demonstrated excellent diagnostic performance, achieving 100% sensitivity and 97% specificity in distinguishing the four major hemoglobin phenotypes, using high-performance liquid chromatography (HPLC) as the gold standard. Ongoing multicenter validation is being conducted in collaboration with RED Africa and three SCD clinics in Kinshasa, Democratic Republic of the Congo, evaluating performance across neonatal and adult populations. Requiring just a few standard salts (buffer), minimal sample manipulation (drop deposition and drying), and a smartphone as the sole necessary technical element, this method combines test reliability, cost-effectiveness, and accessibility at a level never attained so far. This scalable, cost-effective point-of-care platform offers significant promise for expanding equitable SCD screening and early diagnosis in low-resource, high-prevalence regions

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