Smartphone-integrated portable microfluidic platform for liver biomarker quantification using deep learning.

Accurate and decentralized liver biomarker testing is critical for early diagnosis and monitoring of hepatic dysfunctions, particularly in resource-constrained settings. This work presents a novel smartphone-integrated colorimetric sensing platform that combines microfluidics, deep learning, and mobile health technologies to estimate liver biomarkers quantitatively. A stereolithography (SLA) 3D-printed microfluidic flow cell, optimized for low reagent use and high optical clarity, processes 100 µL of sample-reagent mixture via a peristaltic pump at 50 µL/s. Biomarker-specific chromogenic reactions are imaged within a controlled lighting enclosure using multiple smartphone models and analyzed using a convolutional neural network (CNN) for a regression approach. The system achieves clinically relevant detection ranges of 0.1-20 mg/dL for direct and total bilirubin, and 10-300 U/L for alanine aminotransferase (ALT) and aspartate aminotransferase (AST), with limits of detection of 0.1 mg/dL, 0.05 mg/dL, 2.97 U/L, and 2.5 U/L, respectively. A two-point smartphone adaptability framework ensures robust cross-device performance without retraining. An Android application has been developed, which provides users with disease identification, real-time inference, and visualization of result. This clinical-grade analyzer features an average coefficient of determination (R