Authors
Supriya Atta, Taylor L Thorsen, Tyler Vasse, Vanessa K Berner, Marcellene A Gates-Hollingsworth, Haydon J Hill, David P AuCoin, Xiaoshan Zhu, Tuan Vo-Dinh
Published in
ACS applied materials & interfaces. Sep 21, 2026. Epub Sep 21, 2026.
Abstract
Lateral flow immunoassays (LFIAs) are widely used in point-of-care diagnostics for their low cost and simplicity, but conventional formats rely on subjective visual readout and lack quantitative accuracy and sensitivity. Here, we present a smartphone-guided, artificial-intelligence-enhanced LFIA platform that unites a rationally engineered dual-modal nanoprobe with on-device machine learning, demonstrated for quantitative detection of Rift Valley fever virus (RVFV). The platform combines three synergistic principles: advanced magnetic quantum dot nanoparticles (AQDs) that integrate a magnetic core and a quantum dot shell in a single label to provide both colorimetric and fluorescence signals while enabling magnetic preconcentration of the target antigen; multi-illumination imaging that turns an ordinary smartphone camera into a quantitative reader; and a machine-learning model that extracts illumination-robust features to convert signal into concentration, removing user subjectivity and lighting sensitivity. This design markedly improves sensitivity over conventional gold-nanosphere LFIA in both modes, enables reliable quantification directly in serum without any pretreatment, and outperforms naked-eye interpretation in blind testing. By coupling dual-modal amplification and magnetic enrichment with AI-driven analysis on widely available hardware, the AQD-LFIA platform offers a scalable route toward next-generation point-of-care and at-home diagnostics.
PMID:
42760093
Bibliographic data and abstract were imported from PubMed on 19 Sep 2026.
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