Machine-Learning-Assisted Blood Parameter Sensing Platform for Rapid Next Generation Biomedical and Healthcare Applications

Abstract

The pursuit of rapid diagnosis has resulted in considerable advances in blood parameter sensing technologies. As technology advances, there may be challenges in equitable access for all individuals due to economic constraints, advanced expertise, limited accessibility in particular places, or insufficient infrastructure. Here, a simple, cost-efficient, benchtop biochemical blood-sensing platform was developed for detecting crucial blood parameters for multiple disease diagnosis. Colorimetric and image processing techniques were used to evaluate color intensity. A CMOS image sensor was utilized to capture images to calculate optical density for sensing. The platform was assessed with blood serum samples, including Albumin, Gamma Glutamyl Transferase, Alpha Amylase, Alkaline Phosphatase, Bilirubin, and Total Protein within clinically relevant limits. The platform had excellent limits of detection for these parameters, which are critical for diagnosing liver and kidney-related diseases (0.27g/dL, 0.86IU/L, 1.24IU/L, 0.97IU/L, 0.24mg/dL, 0.35g/dL, respectively). Machine learning algorithms were used to estimate targeted blood parameter concentrations from optical density readings, with 98.48% accuracy and reduced incubation time by nearly 80%. The proposed platform was compared to commercial analyzers, which demonstrate excellent accuracy and reproducibility with remarkable precision (0.03 to 0.71%CV). The platform's robust stability of 99.84% was shown via stability analysis, indicating its practical applicability.