Machine learning-augmented lateral flow assays for point-of-care infectious disease diagnostics

Abstract

Lateral flow assays (LFAs) are among the most widely used point-of-care (PoC) diagnostic platforms for infectious diseases due to their rapid operation, low cost, and user-friendly architecture. However, conventional LFAs remain limited by analytical sensitivity, qualitative or semi-quantitative outputs, and reliance on subjective visual interpretation. Recent innovations in nanomaterial engineering, signal amplification strategies, and multiplex assay design have significantly improved detection performance across viral, bacterial, and other pathogens. Advanced labels, CRISPR-assisted amplification, and dual-mode sensing formats have expanded the analytical capabilities of LFAs beyond traditional colorimetric designs. Parallel to material and biochemical advancements, AI and machine learning (ML)-based image analysis have emerged as transformative tools for digital LFA interpretation. Smartphone-assisted readers and convolutional neural networks (CNNs) enable objective, quantitative signal extraction, reduce user-dependent variability, and improve detection of weak test lines. These approaches support standardized analysis and scalable disease surveillance. Despite these advances, challenges remain in sensitivity optimization, dataset quality, standardization, and regulatory alignment of ML-enabled diagnostic platforms. Future integration of AI-driven analytics with robust assay engineering is expected to redefine LFA platforms as digitally connected, quantitative, and clinically reliable PoC diagnostic systems.