Machine learning-integrated lateral flow assays: Unlocking the future of intelligent point-of-care sensing

• Published in: TrAC, Trends in analytical chemistry (Regular ed.) Vol. 193; p. 118478
• Main Authors: Sarathkumar, Elangovan, Jayasree, Ramapurath S.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.12.2025
• Subjects: Artificial intelligence; Biosensor; Colorimetric; Fluorescence and SERS LFAs; Lateral flow assay; Machine learning; Point-of-care diagnostics; Point-of-care diagnostics; Biosensor; Machine learning; Lateral flow assay; Fluorescence and SERS LFAs; Artificial intelligence; Colorimetric
• ISSN: 0165-9936
• DOI: 10.1016/j.trac.2025.118478

Lateral flow assays (LFAs) have evolved from simple, rapid tests into sophisticated platforms capable of high-sensitivity biomarker detection. Advanced formats such as nanozyme-based, fluorescence, surface-enhanced Raman scattering (SERS), and electrochemical LFAs now offer unprecedented analytical capabilities but also produce complex signal outputs that challenge traditional interpretation methods. This raises a critical question on which computational strategies can unlock their full potential. Machine learning (ML) and deep learning (DL) provide powerful solutions, enabling automated, quantitative, and intelligent analysis. In this review, we critically assess the suitability of different ML algorithms for each LFA format, highlighting their strengths, limitations, and implementation considerations. Unlike broader reviews on point-of-care diagnostics, our work focuses exclusively on LFAs, illustrating how ML can enhance sensitivity, minimise user dependency, enable multiplexing, and support real-world deployment. By addressing challenges of data variability, standardization gaps as well as integration into regulatory and healthcare frameworks, we outline the role of ML in shaping robust, next-generation intelligent LFA platforms. [Display omitted] •Machine learning and Deep learning (ML/DL) in nanozyme-based, fluorescence, SERS, and electrochemical LFAs.•Practical guidance on selecting ML/DL algorithms for LFA formats and data types.•Distinguishes algorithm suitability for quantification versus classification tasks.•Evaluates analytical performance improvements enabled by ML/DL models.•Addresses limitations, challenges, and real-world deployment of intelligent LFAs.