Automated, Deep Learning‐Enabled Immunoassay Microfluidic Cartridge for Viral Pathogen Detection
Enzyme-linked immunosorbent assays (ELISA) are recognized for their accuracy and versatility in disease diagnostics. However, conventional ELISA protocols rely on laboratory equipment, trained personnel, and established infrastructure, restricting their use to complex clinical environments, making them impractical for at-home diagnostics. Here, VISTA, a disposable, electricity-free microfluidic chip designed for immunoassay-based point-of-care diagnostic applications is reported. VISTA enables a bubbling immunoassay-based assay to be completed, sample-to-result, in under 45 min in non-laboratory settings. Using pressure-driven microfluidics, the device manipulates antibody-conjugated magnetic beads and platinum nanoparticles to execute a streamlined semiautomated immunoassay process with minimal user input. VISTA consolidates all steps, including sample processing and reagent addition, into a single, low-cost cartridge with user-friendly features to ensure proper operation. It pairs with an AI-enabled smartphone application, utilizing an adversarial neural network, that automatically interprets assay results from a smartphone image, eliminating the need for specialized expertise. The device's capability is demonstrated through the bubbling immunoassay to detect SARS-CoV-2 N antigen and HCV core antigen directly from 52 patient samples. VISTA's sensitivity is on par with lab-based ELISAs and surpasses conventional point-of-care technologies, detecting viral loads less than 10<sup>4</sup> copies mL<sup>-1</sup>. This solution provides sensitive and cost-effective diagnostics, ideal for use in low-resource settings.
Authors
- Zhimin Yin (ORCID: https://orcid.org/0000-0003-4255-5015)
- Raymond T. Chung (ORCID: https://orcid.org/0000-0001-7587-718X)
- Manoj Kumar Kanakasabapathy (ORCID: https://orcid.org/0000-0002-0756-8585)
- Joseph Hardie (ORCID: https://orcid.org/0000-0002-7206-6581)
- Ahmed Shokr
- Hadi Shafiee (ORCID: https://orcid.org/0000-0003-2240-7648)
- Daniel R. Kuritzkes (ORCID: https://orcid.org/0000-0001-6196-5883)
- E. Weinmann
- Juhyeon Chun (ORCID: https://orcid.org/0009-0009-5889-6376)
- Prudhvi Thirumalaraju (ORCID: https://orcid.org/0000-0002-0573-0243)
- Sungwan Kim (ORCID: https://orcid.org/0000-0001-8090-199X)
- Jane J. Kim (ORCID: https://orcid.org/0000-0002-9892-9170)
- G.T. Gallagher
- Aditya Vardhan Reddy Katkuri (ORCID: https://orcid.org/0009-0008-4519-5856)
- J. Z. Li
- Jorge Mera (ORCID: https://orcid.org/0000-0003-2865-552X)
- Manasvi Alam (ORCID: https://orcid.org/0000-0002-5368-1060)
- Harshitha Mariadoss Yuvaraj
- Jack Minahan
- Teertha Ayanji
Institutions
- Brigham and Women's Hospital (US)
- Harvard University (US)
- University of New Mexico (US)
- Cherokee Nation (US)
- Massachusetts General Hospital (US)
Publication Details
- Journal
- Advanced Materials Technologies
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1002/admt.202500025
- Citations
- 2
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- National Institutes of Health