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Critical review demonstrates machine learning integration enhances optical biosensing in healthcare and environmental monitoring, highlighting pathways toward next-generation diagnostic tools.
Machine learning, combined with optical biosensing, has developed into advanced optical sensors that have shown the powerful capability of real-time detection for biological and chemical analytes at ultra-low concentrations through biomedical, environmental and healthcare applications. Surface Plasmon Resonance (SPR) based biosensors have attracted much attention among various optical sensing techniques due to their label-free detection, fast respond and high sensitivity for refractive index change. This paper aims to provide a critical review of the machine-learning supported advancements of optical biosensors, with special focus on selection and application of advanced modelling methodologies, recent development in intelligent signal processing as well as next generation of SPR sensing platforms. The study discusses the fundamentals of optical biosensing: SPR theory and important performance parameters such as sensitivity, resolution, figure of merit (FOM) and limit of detection (LOD). Additionally, we review supervised, unsupervised and deep learning algorithms for feature extraction, signal denoising, biomarker detection and sensor optimization. Emerging trends namely IoT enabled biosensors, photonic crystal sensors and explainable AI for next generation intelligent healthcare systems are highlighted.
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Dondapati et al. (2026) studied this question.
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