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Related Experiment Video

Updated: May 7, 2026

Microfluidic Chip Fabrication and Method to Detect Influenza

Microfluidic Chip Fabrication and Method to Detect Influenza

Published on: March 26, 2013

Automated, Deep Learning-Enabled Immunoassay Microfluidic Cartridge for Viral Pathogen Detection.

Joseph Michael Hardie¹, Sungwan Kim¹, Zehua Yin¹

¹Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

Advanced Materials Technologies

|October 6, 2025

Summary

A new microfluidic chip, VISTA, enables rapid, electricity-free point-of-care diagnostics using a smartphone. This disposable device offers lab-comparable sensitivity for infectious disease detection outside clinical settings.

Keywords:

artificial intelligence diagnostic microfluidics nanoparticles

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Last Updated: May 7, 2026

Microfluidic Chip Fabrication and Method to Detect Influenza

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Published on: March 26, 2013

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Published on: June 23, 2022

Area of Science:

Biotechnology
Medical Diagnostics
Microfluidics

Background:

Conventional Enzyme-linked immunosorbent assays (ELISA) require specialized equipment and trained personnel, limiting their use in resource-limited or at-home settings.
Point-of-care diagnostic technologies often lack the sensitivity and accuracy of laboratory-based methods.

Purpose of the Study:

To develop a disposable, electricity-free microfluidic chip (VISTA) for rapid, automated immunoassay-based diagnostics at the point-of-care.
To integrate VISTA with an AI-enabled smartphone application for simplified result interpretation.

Main Methods:

VISTA utilizes pressure-driven microfluidics, magnetic beads, and platinum nanoparticles for a semiautomated immunoassay process.
The device consolidates sample processing and reagent addition into a single cartridge.
An AI-powered smartphone application with an adversarial neural network analyzes assay results from images.

Main Results:

VISTA successfully detected SARS-CoV-2 N antigen and HCV core antigen in 52 patient samples within 45 minutes.
The diagnostic sensitivity of VISTA is comparable to laboratory-based ELISAs.
VISTA demonstrated superior sensitivity compared to existing point-of-care technologies, detecting viral loads below 10^4 copies/mL.

Conclusions:

VISTA offers a sensitive, cost-effective, and user-friendly solution for point-of-care diagnostics, particularly in low-resource settings.
The integration of microfluidics and AI enables accessible disease detection without specialized expertise or equipment.
This technology has the potential to significantly improve diagnostic capabilities in diverse healthcare environments.