Decentralized diagnostics is undergoing a transformative shift from qualitative screening to high-precision quantification, driven by the clinical demand for rapid, point-of-care (POC) syndromic triage. Multiplexed lateral flow immunoassays (mLFIAs) serve as the foundational platform for this transition. However, their performance is limited by systemic factors such as fluidic lag, conjugate depletion, and spectral crosstalk. This review evaluates recent advances in engineered nanomaterials and artificial intelligence (AI)-driven detection as the dual pillars of next-generation multiplexing. The review covers different types of nanomaterial reporters—such as multicolor quantum dots, surface-enhanced Raman scattering nanotags, upconversion nanoparticles, surface-modified magnetic nanoparticles, and fluorescent nanodiamonds—that help address analytical challenges in lateral flow assays. We then discuss AI and machine learning methods, including convolutional neural networks, support vector machines, random forests, and transfer learning, that convert raw multi-channel signals into useful clinical data. Finally, we highlight the main challenges that still need to be addressed before these platforms can become WHO-ASSURED-compliant POC devices. The combination of engineered nanomaterial reporters and computational intelligence is transforming lateral flow assays into quantitative tools that can provide lab-quality clinical information at the POC.
Huynh et al. (Thu,) studied this question.