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Updated: May 25, 2026

Low-Cost, Volume-Controlled Dipstick Urinalysis for Home-Testing

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Smartphone-Based Microbubble-Linked Immunosorbent Assay Powered by Classification-Regression Integrated Deep Learning

Xin Tang¹, Lian Xue¹, Xiang Liu¹

¹The Key Laboratory for Biomedical Photonics of MOE at Wuhan National Laboratory for Optoelectronics - Hubei Bioinformatics & Molecular Imaging Key Laboratory, Systems Biology Theme, Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.

|May 5, 2026

Summary

This summary is machine-generated.

A new smartphone-based platform, CRiB^DL-ELISA, enables precise and cost-effective biomarker detection. This portable diagnostic tool utilizes deep learning for rapid analysis, improving healthcare accessibility.

Keywords:

ELISA POCT biomarker quantification bubble signal deep learning

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Area of Science:

Biomedical Engineering
Point-of-Care Diagnostics
Artificial Intelligence in Healthcare

Background:

Traditional ELISA requires expensive equipment, limiting accessibility.
Growing demand for portable and efficient diagnostic technologies.
Need for cost-effective and robust biomarker detection methods.

Purpose of the Study:

To develop a smartphone-based platform for precise biomarker detection using deep learning.
To overcome limitations of traditional ELISA systems.
To provide a cost-effective, portable, and high-throughput diagnostic solution.

Main Methods:

Development of CRiB^DL-ELISA (Classification-Regression Integrated Deep Learning Microbubble-Based Enzyme-Linked Immunosorbent Assay) platform.
Utilized platinum nanoprobes for bubble pattern generation upon biomarker capture.
Integrated smartphone imaging, YOLO object detection, and deep learning for analysis.
Quantification based on concentration-dependent bubble patterns.

Main Results:

Achieved low detection limits (0.001 ng/mL to 0.0001 ng/mL) for biomarkers like CRP, PCT, NT-proBNP, and cTnI.
Demonstrated high accuracy with R² values up to 0.9983.
Clinical validation showed high accuracy for CRP (100.00%) and NT-proBNP (97.22%).
NT-proBNP measurements showed strong concordance with commercial platforms (R² = 0.97).

Conclusions:

CRiB^DL-ELISA offers a cost-effective, robust, and portable solution for clinical diagnostics.
The platform enables high-throughput biomarker quantification without specialized instrumentation.
Smartphone-based deep learning analysis enhances precision and accessibility of diagnostic tests.