A Cost-Effective Approach For Real-Time Anemia Diagnosis Using An Automated Image Processing Tool Interfaced Paper Sensor

A large proportion of the world’s population from low and middle-income countries, particularly women of reproductive age and young children, are vulnerable to anemia. Anemia reduces work productivity and increases mortality in chronic ailments such as kidney, cardiovascular, acquired immunodeficiency diseases, etc. Therefore, mass screening of anemic population and early treatment are crucial for public health programs. While various POC device has been reported earlier for rapid, affordable sensing of anemia, these methods have several limitations that hinder their broad implementation in practical applications. Here, we report an on-card approach for anemia detection by measuring the blood-spreading area on a cellulose paper platform. The image analysis was coupled with a Python-based image processing tool for the automated measurement of blood Hb. This device was standardized using a minimal blood volume of 10μl and demonstrates a sensitivity of -5.17 ± 0.11 (mm
²
/(g/dl)) and can efficiently measure Hb as low as 1.07g/dl. On comparison with the gold standard method, strong correlation(r=0.99) and good accuracy (mean bias= 0.15g/dl with a narrow limit of agreements, i.e., -0.67 to 0.98 g/dl) was achieved. This device operates reagent-free and thus is not susceptible to interference caused by storing and transportation. Automated analysis precluded the chance of error due to manual handling, thus reducing the risk of misdiagnosis. The developed paper sensor offers a simple, affordable, automated method for real-time anemia detection and potentially facilitates anemia screening in poor settings.